{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30cd60f3",
   "metadata": {},
   "source": [
    "# Where Ontology Similarity and Text-Embedding Similarity Disagree\n",
    "\n",
    "For a sample of term pairs in each of several ontologies, we compare:\n",
    "\n",
    "- **ontology similarity**, computed from the terms' ancestor closures in three ways:\n",
    "  1. Jaccard over `is_a` ancestors;\n",
    "  2. Jaccard over `is_a` + `part_of` ancestors;\n",
    "  3. **IC-weighted** Jaccard over `is_a` + `part_of` ancestors (simGIC), in which each\n",
    "     shared ancestor counts in proportion to its information content, so sharing a\n",
    "     generic ancestor counts for little;\n",
    "- **text similarity**: cosine similarity of the terms' LLM embeddings, as served by OLS.\n",
    "\n",
    "Most pairs fall along a common trend. The interesting cases are in the **corners**,\n",
    "where ontology and text disagree:\n",
    "\n",
    "- **ontology-similar, text-dissimilar**: terms that are close in the hierarchy but\n",
    "  described in different words;\n",
    "- **text-similar, ontology-dissimilar**: terms that read alike but are classified far\n",
    "  apart.\n",
    "\n",
    "All three ontology measures are available through OAK's `EmbeddingProviderInterface`:\n",
    "the `closure` and `closure_ic` models produce the vectors, with\n",
    "`closure_embedding_predicates` controlling which relations are followed, and the\n",
    "`jaccard` / `weighted_jaccard` metrics compare them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01f772d9",
   "metadata": {
    "execution": {
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"eutils\")\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# reference palette (categorical slots in fixed order; sequential blue ramp)\n",
    "BLUE, ORANGE, AQUA = \"#2a78d6\", \"#eb6834\", \"#1baf7a\"\n",
    "INK, INK2, GRID, SURFACE = \"#0b0b0b\", \"#52514e\", \"#e4e3df\", \"#fcfcfb\"\n",
    "plt.rcParams.update({\n",
    "    \"figure.facecolor\": SURFACE, \"axes.facecolor\": SURFACE, \"savefig.facecolor\": SURFACE,\n",
    "    \"axes.edgecolor\": GRID, \"axes.labelcolor\": INK2, \"axes.titlecolor\": INK,\n",
    "    \"axes.grid\": True, \"grid.color\": GRID, \"grid.linewidth\": 0.6,\n",
    "    \"axes.spines.top\": False, \"axes.spines.right\": False,\n",
    "    \"xtick.color\": INK2, \"ytick.color\": INK2, \"text.color\": INK,\n",
    "    \"font.size\": 10, \"axes.titlesize\": 11, \"figure.dpi\": 110,\n",
    "})\n",
    "SEQUENTIAL = sns.blend_palette([\"#f0efec\", \"#86b6ef\", \"#2a78d6\", \"#0d366b\"], as_cmap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3c8cf15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T15:28:47.403853Z",
     "iopub.status.busy": "2026-10-09T15:28:47.403495Z",
     "iopub.status.idle": "2026-10-09T15:28:50.413847Z",
     "shell.execute_reply": "2026-10-09T15:28:50.412395Z"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from oaklib import get_adapter\n",
    "from oaklib.datamodels.vocabulary import IS_A, OWL_CLASS, PART_OF\n",
    "\n",
    "MODEL = \"text-embedding-3-large_pca512\"\n",
    "# ontology -> (id prefix, root to sample within; None for the whole ontology)\n",
    "ONTOLOGIES = {\n",
    "    \"hp\": (\"HP\", \"HP:0000118\"),  # Phenotypic abnormality\n",
    "    \"mondo\": (\"MONDO\", None),\n",
    "    \"go\": (\"GO\", None),\n",
    "    \"cl\": (\"CL\", None),\n",
    "}\n",
    "N_RELATED, N_RANDOM, N_SHOW = 300, 100, 20\n",
    "MEASURES = {\n",
    "    \"jaccard_isa\": \"Jaccard (is_a)\",\n",
    "    \"jaccard_isa_partof\": \"Jaccard (is_a + part_of)\",\n",
    "    \"simgic\": \"IC-weighted Jaccard (is_a + part_of)\",\n",
    "}\n",
    "PRIMARY = \"simgic\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17d74b6",
   "metadata": {},
   "source": [
    "## Sampling pairs\n",
    "\n",
    "Purely random pairs are almost all unrelated, so they would pile up in one corner. To\n",
    "cover the whole range of ontology similarity, most pairs are sampled by picking a term,\n",
    "moving up to a random one of its `is_a` ancestors, then down to a random descendant of\n",
    "that ancestor. A smaller number of fully random pairs is added. Only terms from the\n",
    "ontology's own namespace are used (no imported terms), obsolete terms are excluded, and\n",
    "for HP only terms under *Phenotypic abnormality* are used (no clinical modifiers,\n",
    "inheritance modes, etc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1306cf74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T15:28:50.417561Z",
     "iopub.status.busy": "2026-10-09T15:28:50.416842Z",
     "iopub.status.idle": "2026-10-09T15:36:03.306039Z",
     "shell.execute_reply": "2026-10-09T15:36:03.303727Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hp 400 pairs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mondo 400 pairs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go 400 pairs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cl 400 pairs\n"
     ]
    }
   ],
   "source": [
    "def sample_pairs(adapter, prefix, root=None, seed=0):\n",
    "    rng = random.Random(seed)\n",
    "    if root:\n",
    "        candidates = adapter.descendants(root, predicates=[IS_A], reflexive=False)\n",
    "    else:\n",
    "        candidates = adapter.entities(filter_obsoletes=True, owl_type=OWL_CLASS)\n",
    "    terms = sorted(t for t in candidates if t.startswith(prefix + \":\"))\n",
    "    term_set = set(terms)\n",
    "    pairs = set()\n",
    "    while len(pairs) < N_RELATED:\n",
    "        a = rng.choice(terms)\n",
    "        ancs = [x for x in adapter.ancestors(a, predicates=[IS_A], reflexive=False) if x in term_set]\n",
    "        if not ancs:\n",
    "            continue\n",
    "        x = rng.choice(ancs)\n",
    "        descs = [d for d in adapter.descendants(x, predicates=[IS_A], reflexive=False)\n",
    "                 if d in term_set and d != a]\n",
    "        if descs:\n",
    "            pairs.add(tuple(sorted((a, rng.choice(descs)))))\n",
    "    while len(pairs) < N_RELATED + N_RANDOM:\n",
    "        a, b = rng.sample(terms, 2)\n",
    "        pairs.add(tuple(sorted((a, b))))\n",
    "    return sorted(pairs)\n",
    "\n",
    "adapters, pair_sets = {}, {}\n",
    "for name, (prefix, root) in ONTOLOGIES.items():\n",
    "    adapters[name] = (get_adapter(f\"sqlite:obo:{name}\"), get_adapter(f\"ols:{name}\"))\n",
    "    pair_sets[name] = sample_pairs(adapters[name][0], prefix, root)\n",
    "    print(name, len(pair_sets[name]), \"pairs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541e6920",
   "metadata": {},
   "source": [
    "## Scoring\n",
    "\n",
    "For each ontology, closure vectors for all sampled terms are computed in one call so that\n",
    "they share dimensions. Information content (IC) is computed from the ontology structure\n",
    "(how many terms each ancestor subsumes), over the same `is_a` + `part_of` closure.\n",
    "In the IC-weighted vectors, the shared position with the largest value is the **most\n",
    "informative common ancestor** (MICA); it is listed in the tables to show where the\n",
    "ontology places the pair.\n",
    "\n",
    "The same vectors are available directly from the adapter:\n",
    "\n",
    "```python\n",
    "onto.closure_embedding_predicates = [IS_A, PART_OF]\n",
    "ids, matrix = onto.entity_embeddings(terms, model=\"closure_ic\")\n",
    "onto.embedding_similarity_matrix(terms, model=\"closure_ic\", metric=\"weighted_jaccard\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66466fde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T15:36:03.309450Z",
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     "iopub.status.idle": "2026-10-09T15:36:14.409584Z",
     "shell.execute_reply": "2026-10-09T15:36:14.408720Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hp: 400 pairs scored (0 missing a text embedding)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mondo: 400 pairs scored (0 missing a text embedding)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go: 400 pairs scored (0 missing a text embedding)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cl: 400 pairs scored (0 missing a text embedding)\n"
     ]
    }
   ],
   "source": [
    "from oaklib.utilities.embeddings.closure_embeddings import closure_embeddings\n",
    "\n",
    "PREDICATES = [IS_A, PART_OF]\n",
    "\n",
    "def jaccard(x, y):\n",
    "    x, y = x > 0, y > 0\n",
    "    return (x & y).sum() / (x | y).sum()\n",
    "\n",
    "def weighted_jaccard(x, y):\n",
    "    return np.minimum(x, y).sum() / np.maximum(x, y).sum()\n",
    "\n",
    "frames, micas = [], {}\n",
    "for name, (onto, ols) in adapters.items():\n",
    "    pairs = pair_sets[name]\n",
    "    terms = sorted({t for p in pairs for t in p})\n",
    "    ids1, _, C1 = closure_embeddings(onto, terms, predicates=[IS_A])\n",
    "    ids2, vocab, C2 = closure_embeddings(onto, terms, predicates=PREDICATES)\n",
    "    ic = dict(onto.information_content_scores(vocab, object_closure_predicates=PREDICATES))\n",
    "    _, _, W = closure_embeddings(onto, ids2, predicates=PREDICATES, vocabulary=vocab, weights=ic)\n",
    "    ix1, ix2 = {t: i for i, t in enumerate(ids1)}, {t: i for i, t in enumerate(ids2)}\n",
    "    tids, T = ols.entity_embeddings(terms, model=MODEL)\n",
    "    T = T / np.linalg.norm(T, axis=1, keepdims=True)\n",
    "    tix = {t: i for i, t in enumerate(tids)}\n",
    "    rows = []\n",
    "    for a, b in pairs:\n",
    "        if a not in tix or b not in tix:\n",
    "            continue\n",
    "        wa, wb = W[ix2[a]], W[ix2[b]]\n",
    "        shared = np.minimum(wa, wb)\n",
    "        micas[(a, b)] = vocab[int(shared.argmax())] if shared.max() > 0 else None\n",
    "        rows.append({\n",
    "            \"ontology\": name, \"a\": a, \"b\": b,\n",
    "            \"jaccard_isa\": jaccard(C1[ix1[a]], C1[ix1[b]]),\n",
    "            \"jaccard_isa_partof\": jaccard(C2[ix2[a]], C2[ix2[b]]),\n",
    "            \"simgic\": weighted_jaccard(wa, wb),\n",
    "            \"text_sim\": float(T[tix[a]] @ T[tix[b]]),\n",
    "        })\n",
    "    df = pd.DataFrame(rows)\n",
    "    # disagreement: difference of within-ontology percentile ranks, per measure\n",
    "    for m in MEASURES:\n",
    "        df[f\"disagreement_{m}\"] = df[m].rank(pct=True) - df.text_sim.rank(pct=True)\n",
    "    frames.append(df)\n",
    "    print(f\"{name}: {len(df)} pairs scored ({len(pairs) - len(df)} missing a text embedding)\")\n",
    "scores = pd.concat(frames, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77b73863",
   "metadata": {},
   "source": [
    "## How well does each ontology measure agree with text?\n",
    "\n",
    "Spearman correlation between each ontology measure and text similarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "51a0de40",
   "metadata": {
    "execution": {
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jaccard (is_a)</th>\n",
       "      <th>Jaccard (is_a + part_of)</th>\n",
       "      <th>IC-weighted Jaccard (is_a + part_of)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ontology</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HP</th>\n",
       "      <td>0.748</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONDO</th>\n",
       "      <td>0.628</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO</th>\n",
       "      <td>0.706</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL</th>\n",
       "      <td>0.662</td>\n",
       "      <td>0.598</td>\n",
       "      <td>0.672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Jaccard (is_a)  Jaccard (is_a + part_of)  \\\n",
       "ontology                                             \n",
       "HP                 0.748                     0.748   \n",
       "MONDO              0.628                     0.628   \n",
       "GO                 0.706                     0.725   \n",
       "CL                 0.662                     0.598   \n",
       "\n",
       "          IC-weighted Jaccard (is_a + part_of)  \n",
       "ontology                                        \n",
       "HP                                       0.789  \n",
       "MONDO                                    0.732  \n",
       "GO                                       0.760  \n",
       "CL                                       0.672  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import spearmanr\n",
    "\n",
    "agreement = pd.DataFrame({\n",
    "    label: scores.groupby(\"ontology\", sort=False).apply(lambda d, m=m: spearmanr(d[m], d.text_sim).statistic)\n",
    "    for m, label in MEASURES.items()\n",
    "})\n",
    "agreement.index = agreement.index.str.upper()\n",
    "agreement.round(3)"
   ]
  },
  {
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   "execution_count": 6,
   "id": "2325738a",
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1540x1870 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(len(ONTOLOGIES), len(MEASURES), figsize=(14, 17), sharey=True)\n",
    "for row, (name, df) in zip(axes, scores.groupby(\"ontology\", sort=False)):\n",
    "    for ax, (m, label) in zip(row, MEASURES.items()):\n",
    "        ax.scatter(df[m], df.text_sim, s=10, color=BLUE, alpha=0.4, linewidths=0)\n",
    "        rho = spearmanr(df[m], df.text_sim).statistic\n",
    "        ax.set_title(f\"{name.upper()}: {label}\\nSpearman ρ = {rho:.2f}\", fontsize=10)\n",
    "        ax.set_xlim(-0.03, 1.03)\n",
    "for ax in axes[:, 0]:\n",
    "    ax.set_ylabel(\"text similarity (cosine)\")\n",
    "for ax in axes[-1]:\n",
    "    ax.set_xlabel(\"ontology similarity\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc897b20",
   "metadata": {},
   "source": [
    "## The corners (IC-weighted Jaccard vs text)\n",
    "\n",
    "The **disagreement** score is the difference between a pair's percentile rank under\n",
    "ontology similarity and its percentile rank under text similarity. The 20 most extreme\n",
    "pairs in each direction are highlighted and numbered; the tables below list them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95787493",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T15:36:17.149684Z",
     "iopub.status.busy": "2026-10-09T15:36:17.149303Z",
     "iopub.status.idle": "2026-10-09T15:36:18.540715Z",
     "shell.execute_reply": "2026-10-09T15:36:18.539311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1430x1265 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ONTO_ONLY, TEXT_ONLY = ORANGE, AQUA\n",
    "d_col = f\"disagreement_{PRIMARY}\"\n",
    "corners = {}\n",
    "fig, axes = plt.subplots(2, 2, figsize=(13, 11.5), sharex=True, sharey=True)\n",
    "for ax, (name, df) in zip(axes.flat, scores.groupby(\"ontology\", sort=False)):\n",
    "    onto_only = df.nlargest(N_SHOW, d_col)\n",
    "    text_only = df.nsmallest(N_SHOW, d_col)\n",
    "    corners[name] = (onto_only, text_only)\n",
    "    rest = df.drop(onto_only.index.union(text_only.index))\n",
    "    ax.scatter(rest[PRIMARY], rest.text_sim, s=12, color=BLUE, alpha=0.35, linewidths=0)\n",
    "    for sub, color, tag in ((onto_only, ONTO_ONLY, \"O\"), (text_only, TEXT_ONLY, \"T\")):\n",
    "        ax.scatter(sub[PRIMARY], sub.text_sim, s=40, color=color, edgecolors=SURFACE, linewidths=1.2, zorder=3)\n",
    "        for k, (_, r) in enumerate(sub.iterrows(), 1):\n",
    "            ax.annotate(f\"{tag}{k}\", (r[PRIMARY], r.text_sim), xytext=(3, 2),\n",
    "                        textcoords=\"offset points\", fontsize=6.5, color=INK2)\n",
    "    rho = spearmanr(df[PRIMARY], df.text_sim).statistic\n",
    "    ax.set_title(f\"{name.upper()}  (n = {len(df)}, Spearman ρ = {rho:.2f})\")\n",
    "for ax in axes[-1]:\n",
    "    ax.set_xlabel(f\"ontology similarity ({MEASURES[PRIMARY]})\")\n",
    "for ax in axes[:, 0]:\n",
    "    ax.set_ylabel(f\"text similarity (cosine, {MODEL.replace('_pca512', '')})\")\n",
    "handles = [plt.Line2D([], [], marker=\"o\", linestyle=\"\", color=c, markersize=7) for c in (BLUE, ONTO_ONLY, TEXT_ONLY)]\n",
    "fig.legend(handles, [\"other pairs\", \"O: ontology-similar, text-dissimilar\", \"T: text-similar, ontology-dissimilar\"],\n",
    "           loc=\"upper center\", ncol=3, frameon=False, bbox_to_anchor=(0.5, 1.0))\n",
    "fig.tight_layout(rect=(0, 0, 1, 0.965))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f3694e",
   "metadata": {},
   "source": [
    "How stable are the corners across ontology measures? For each ontology, the number of the\n",
    "top-20 pairs in each corner that are also in the top 20 under the other measures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f9ecfd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T15:36:18.543679Z",
     "iopub.status.busy": "2026-10-09T15:36:18.543427Z",
     "iopub.status.idle": "2026-10-09T15:36:18.592419Z",
     "shell.execute_reply": "2026-10-09T15:36:18.590914Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>shared with Jaccard (is_a)</th>\n",
       "      <th>shared with Jaccard (is_a + part_of)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ontology</th>\n",
       "      <th>corner</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HP</th>\n",
       "      <th>ontology-similar, text-dissimilar</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text-similar, ontology-dissimilar</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MONDO</th>\n",
       "      <th>ontology-similar, text-dissimilar</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text-similar, ontology-dissimilar</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">GO</th>\n",
       "      <th>ontology-similar, text-dissimilar</th>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text-similar, ontology-dissimilar</th>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CL</th>\n",
       "      <th>ontology-similar, text-dissimilar</th>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text-similar, ontology-dissimilar</th>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            shared with Jaccard (is_a)  \\\n",
       "ontology corner                                                          \n",
       "HP       ontology-similar, text-dissimilar                          15   \n",
       "         text-similar, ontology-dissimilar                          11   \n",
       "MONDO    ontology-similar, text-dissimilar                          17   \n",
       "         text-similar, ontology-dissimilar                          11   \n",
       "GO       ontology-similar, text-dissimilar                          14   \n",
       "         text-similar, ontology-dissimilar                          11   \n",
       "CL       ontology-similar, text-dissimilar                          13   \n",
       "         text-similar, ontology-dissimilar                           7   \n",
       "\n",
       "                                            shared with Jaccard (is_a + part_of)  \n",
       "ontology corner                                                                   \n",
       "HP       ontology-similar, text-dissimilar                                    15  \n",
       "         text-similar, ontology-dissimilar                                    11  \n",
       "MONDO    ontology-similar, text-dissimilar                                    17  \n",
       "         text-similar, ontology-dissimilar                                    11  \n",
       "GO       ontology-similar, text-dissimilar                                    16  \n",
       "         text-similar, ontology-dissimilar                                    14  \n",
       "CL       ontology-similar, text-dissimilar                                    15  \n",
       "         text-similar, ontology-dissimilar                                    14  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = []\n",
    "for name, df in scores.groupby(\"ontology\", sort=False):\n",
    "    for corner, pick in ((\"ontology-similar, text-dissimilar\", \"nlargest\"), (\"text-similar, ontology-dissimilar\", \"nsmallest\")):\n",
    "        top = {m: set(getattr(df, pick)(N_SHOW, f\"disagreement_{m}\").index) for m in MEASURES}\n",
    "        rows.append({\"ontology\": name.upper(), \"corner\": corner,\n",
    "                     **{f\"shared with {MEASURES[m]}\": len(top[PRIMARY] & top[m]) for m in MEASURES if m != PRIMARY}})\n",
    "pd.DataFrame(rows).set_index([\"ontology\", \"corner\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "701a44a2",
   "metadata": {},
   "source": [
    "## The corner pairs\n",
    "\n",
    "For each ontology, the two tables list the 20 most extreme disagreements under the\n",
    "IC-weighted measure, with all three ontology scores. \"MICA\" is the most informative\n",
    "common ancestor over `is_a` + `part_of`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4cea4d10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-09T15:36:18.596312Z",
     "iopub.status.busy": "2026-10-09T15:36:18.595894Z",
     "iopub.status.idle": "2026-10-09T15:36:21.986284Z",
     "shell.execute_reply": "2026-10-09T15:36:21.984957Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### HP: ontology-similar, text-dissimilar"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term 1</th>\n",
       "      <th>term 2</th>\n",
       "      <th>text</th>\n",
       "      <th>IC-Jaccard</th>\n",
       "      <th>Jaccard is_a+part_of</th>\n",
       "      <th>Jaccard is_a</th>\n",
       "      <th>MICA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O1</th>\n",
       "      <td>Slough (HP:0020013)</td>\n",
       "      <td>Trichilemmal cyst (HP:0025246)</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>Localized skin lesion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O2</th>\n",
       "      <td>Light-chain paraproteinemia (HP:0031048)</td>\n",
       "      <td>Increased anti-dust mite IgE antibody level (HP:041...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Abnormal circulating immunoglobulin concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>Fragmented elastic fibers in the dermis (HP:0025167)</td>\n",
       "      <td>V-sign (HP:0025536)</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>Abnormal skin morphology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O4</th>\n",
       "      <td>Abnormality of abdominal situs (HP:0011620)</td>\n",
       "      <td>Isomerism (HP:0031853)</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>Heterotaxy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O5</th>\n",
       "      <td>Abnormal saccadic eye movements (HP:0000570)</td>\n",
       "      <td>Compensatory chin elevation (HP:0001477)</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Abnormality of eye movement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O6</th>\n",
       "      <td>Abnormality of vitamin metabolism (HP:0100508)</td>\n",
       "      <td>Shortened O-fucosylated glycan on properdin (HP:041...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>Abnormal metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O7</th>\n",
       "      <td>Hip pain (HP:0030838)</td>\n",
       "      <td>Fish odor (HP:0410020)</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Constitutional symptom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O8</th>\n",
       "      <td>Recurrent skin infections (HP:0001581)</td>\n",
       "      <td>Autoimmune encephalitis (HP:5210423)</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>Increased inflammatory response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O9</th>\n",
       "      <td>Partial development of the penile shaft (HP:0008708)</td>\n",
       "      <td>Varicocele (HP:0012871)</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.67</td>\n",
       "      <td>Abnormal male external genitalia morphology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O10</th>\n",
       "      <td>CNS hypermyelination (HP:0012754)</td>\n",
       "      <td>Empty delta sign (HP:0032267)</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>Morphological central nervous system abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O11</th>\n",
       "      <td>Granuloma (HP:0032252)</td>\n",
       "      <td>Decreased HLA-DR+ T cell proportion (HP:5200034)</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Abnormal immune system morphology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O12</th>\n",
       "      <td>Geophagia (HP:0025062)</td>\n",
       "      <td>Bulimia (HP:0100739)</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Abnormal eating behavior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O13</th>\n",
       "      <td>Metaphyseal sclerosis (HP:0004979)</td>\n",
       "      <td>Deviation of the 4th toe (HP:0010338)</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>Abnormal limb bone morphology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O14</th>\n",
       "      <td>Anti-transcription intermediary factor-1gamma antib...</td>\n",
       "      <td>Unusual norovirus infection (HP:5210310)</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>Abnormality of immune system physiology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O15</th>\n",
       "      <td>Anti-SEZ6L2 antibody (HP:5000032)</td>\n",
       "      <td>Unusual fungal arthritis (HP:5210209)</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>Abnormality of immune system physiology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O16</th>\n",
       "      <td>Abnormality of superior crus of antihelix (HP:0011245)</td>\n",
       "      <td>Long ear (HP:0400004)</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.67</td>\n",
       "      <td>Abnormal pinna morphology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O17</th>\n",
       "      <td>Reduced eye contact (HP:0000817)</td>\n",
       "      <td>Use of another person's body to communicate (HP:400...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Abnormal nonverbal communicative behavior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O18</th>\n",
       "      <td>Jaw claudication (HP:0030164)</td>\n",
       "      <td>Absent superficial temporal artery pulse (HP:6000786)</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Abnormal arterial physiology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O19</th>\n",
       "      <td>Peg-shaped maxillary lateral incisors (HP:0006342)</td>\n",
       "      <td>Tongue hyperpigmentation (HP:6001529)</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>Abnormal oral morphology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O20</th>\n",
       "      <td>Nephropathy (HP:0000112)</td>\n",
       "      <td>Steroid-dependent nephrotic syndrome (HP:0025650)</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Abnormal renal physiology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     term 1  \\\n",
       "#                                                             \n",
       "O1                                      Slough (HP:0020013)   \n",
       "O2                 Light-chain paraproteinemia (HP:0031048)   \n",
       "O3     Fragmented elastic fibers in the dermis (HP:0025167)   \n",
       "O4              Abnormality of abdominal situs (HP:0011620)   \n",
       "O5             Abnormal saccadic eye movements (HP:0000570)   \n",
       "O6           Abnormality of vitamin metabolism (HP:0100508)   \n",
       "O7                                    Hip pain (HP:0030838)   \n",
       "O8                   Recurrent skin infections (HP:0001581)   \n",
       "O9     Partial development of the penile shaft (HP:0008708)   \n",
       "O10                       CNS hypermyelination (HP:0012754)   \n",
       "O11                                  Granuloma (HP:0032252)   \n",
       "O12                                  Geophagia (HP:0025062)   \n",
       "O13                      Metaphyseal sclerosis (HP:0004979)   \n",
       "O14  Anti-transcription intermediary factor-1gamma antib...   \n",
       "O15                       Anti-SEZ6L2 antibody (HP:5000032)   \n",
       "O16  Abnormality of superior crus of antihelix (HP:0011245)   \n",
       "O17                        Reduced eye contact (HP:0000817)   \n",
       "O18                           Jaw claudication (HP:0030164)   \n",
       "O19      Peg-shaped maxillary lateral incisors (HP:0006342)   \n",
       "O20                                Nephropathy (HP:0000112)   \n",
       "\n",
       "                                                     term 2  text  IC-Jaccard  \\\n",
       "#                                                                               \n",
       "O1                           Trichilemmal cyst (HP:0025246)  0.14        0.31   \n",
       "O2   Increased anti-dust mite IgE antibody level (HP:041...  0.29        0.54   \n",
       "O3                                      V-sign (HP:0025536)  0.03        0.18   \n",
       "O4                                   Isomerism (HP:0031853)  0.28        0.36   \n",
       "O5                 Compensatory chin elevation (HP:0001477)  0.31        0.34   \n",
       "O6   Shortened O-fucosylated glycan on properdin (HP:041...  0.15        0.17   \n",
       "O7                                   Fish odor (HP:0410020)  0.15        0.16   \n",
       "O8                     Autoimmune encephalitis (HP:5210423)  0.20        0.18   \n",
       "O9                                  Varicocele (HP:0012871)  0.38        0.45   \n",
       "O10                           Empty delta sign (HP:0032267)  0.16        0.15   \n",
       "O11        Decreased HLA-DR+ T cell proportion (HP:5200034)  0.14        0.13   \n",
       "O12                                    Bulimia (HP:0100739)  0.44        0.54   \n",
       "O13                   Deviation of the 4th toe (HP:0010338)  0.28        0.24   \n",
       "O14                Unusual norovirus infection (HP:5210310)  0.18        0.15   \n",
       "O15                   Unusual fungal arthritis (HP:5210209) -0.00        0.08   \n",
       "O16                                   Long ear (HP:0400004)  0.41        0.44   \n",
       "O17  Use of another person's body to communicate (HP:400...  0.45        0.49   \n",
       "O18   Absent superficial temporal artery pulse (HP:6000786)  0.44        0.46   \n",
       "O19                   Tongue hyperpigmentation (HP:6001529)  0.27        0.18   \n",
       "O20       Steroid-dependent nephrotic syndrome (HP:0025650)  0.45        0.47   \n",
       "\n",
       "     Jaccard is_a+part_of  Jaccard is_a  \\\n",
       "#                                         \n",
       "O1                   0.60          0.60   \n",
       "O2                   0.72          0.72   \n",
       "O3                   0.38          0.38   \n",
       "O4                   0.50          0.50   \n",
       "O5                   0.62          0.62   \n",
       "O6                   0.40          0.40   \n",
       "O7                   0.43          0.43   \n",
       "O8                   0.33          0.33   \n",
       "O9                   0.67          0.67   \n",
       "O10                  0.42          0.42   \n",
       "O11                  0.31          0.31   \n",
       "O12                  0.75          0.75   \n",
       "O13                  0.47          0.47   \n",
       "O14                  0.40          0.40   \n",
       "O15                  0.22          0.22   \n",
       "O16                  0.67          0.67   \n",
       "O17                  0.73          0.73   \n",
       "O18                  0.70          0.70   \n",
       "O19                  0.39          0.39   \n",
       "O20                  0.73          0.73   \n",
       "\n",
       "                                                  MICA  \n",
       "#                                                       \n",
       "O1                               Localized skin lesion  \n",
       "O2   Abnormal circulating immunoglobulin concentration  \n",
       "O3                            Abnormal skin morphology  \n",
       "O4                                          Heterotaxy  \n",
       "O5                         Abnormality of eye movement  \n",
       "O6                                 Abnormal metabolism  \n",
       "O7                              Constitutional symptom  \n",
       "O8                     Increased inflammatory response  \n",
       "O9         Abnormal male external genitalia morphology  \n",
       "O10   Morphological central nervous system abnormality  \n",
       "O11                  Abnormal immune system morphology  \n",
       "O12                           Abnormal eating behavior  \n",
       "O13                      Abnormal limb bone morphology  \n",
       "O14            Abnormality of immune system physiology  \n",
       "O15            Abnormality of immune system physiology  \n",
       "O16                          Abnormal pinna morphology  \n",
       "O17          Abnormal nonverbal communicative behavior  \n",
       "O18                       Abnormal arterial physiology  \n",
       "O19                           Abnormal oral morphology  \n",
       "O20                          Abnormal renal physiology  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### HP: text-similar, ontology-dissimilar"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
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       "      <th></th>\n",
       "      <th>term 1</th>\n",
       "      <th>term 2</th>\n",
       "      <th>text</th>\n",
       "      <th>IC-Jaccard</th>\n",
       "      <th>Jaccard is_a+part_of</th>\n",
       "      <th>Jaccard is_a</th>\n",
       "      <th>MICA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T1</th>\n",
       "      <td>Osteoma cutis (HP:0025027)</td>\n",
       "      <td>Patchy sclerosis of the proximal phalanx of the 3rd...</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>Synostosis involving the 5th metacarpal (HP:0009708)</td>\n",
       "      <td>Proximal radioulnar joint dislocation (HP:6000890)</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Abnormality of the upper limb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>Bone-in-a-bone appearance of forearm (HP:0003955)</td>\n",
       "      <td>Bullet-shaped middle phalanx of the 3rd finger (HP:...</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>Abnormality of the upper limb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>Psychotic episodes (HP:0000725)</td>\n",
       "      <td>Focal aware emotional seizure with laughing (HP:003...</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>Abnormal nervous system physiology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>Periorbital fullness (HP:0000629)</td>\n",
       "      <td>Lower eyelid edema (HP:0012568)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>Abnormality of the periorbital region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>Hypoplasia of the ovary (HP:0008724)</td>\n",
       "      <td>Increased antral follicle count (HP:0033086)</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>Abnormality of the genital system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>Abnormality of liposaccharide metabolism (HP:0010968)</td>\n",
       "      <td>Abnormality of abdominal situs (HP:0011620)</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>Abnormality of the joint spaces of the elbow (HP:00...</td>\n",
       "      <td>Irregular epiphysis of the proximal phalanx of the ...</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>Abnormality of the upper limb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>Fatigable weakness of bulbar muscles (HP:0030192)</td>\n",
       "      <td>Cervical motion tenderness (HP:6000107)</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10</th>\n",
       "      <td>Chordee (HP:0000041)</td>\n",
       "      <td>Vesicoallantoic abdominal wall defect (HP:0030733)</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>Abnormality of the genitourinary system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T11</th>\n",
       "      <td>Increased circulating lactate concentration (HP:000...</td>\n",
       "      <td>Elevated urinary 5-amino-4-imidazolecarboxamide-rib...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Abnormality of metabolism/homeostasis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T12</th>\n",
       "      <td>Absent middle phalanx of 4th finger (HP:0009294)</td>\n",
       "      <td>Patchy sclerosis of the middle phalanx of the 4th f...</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>Abnormality of the middle phalanx of the 4th finger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T13</th>\n",
       "      <td>Medially fused radial epiphyseal plates (HP:0004013)</td>\n",
       "      <td>Abnormal renal corticomedullary differentiation (HP...</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T14</th>\n",
       "      <td>Fibrous tissue neoplasm (HP:0012316)</td>\n",
       "      <td>Neoplasm of the parathyroid gland (HP:0100733)</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>Neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T15</th>\n",
       "      <td>Chiari type I malformation (HP:0007099)</td>\n",
       "      <td>Abdominal aortic dissection (HP:0033311)</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T16</th>\n",
       "      <td>Adrenal pheochromocytoma (HP:0006748)</td>\n",
       "      <td>Uterine adenofibroma (HP:0034709)</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Neoplasm by anatomical site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T17</th>\n",
       "      <td>Unilateral radial aplasia (HP:0011908)</td>\n",
       "      <td>Primary vesicoureteral reflux (HP:0033738)</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T18</th>\n",
       "      <td>Attached earlobe (HP:0009907)</td>\n",
       "      <td>Splayed superior cerebellar peduncle (HP:0030285)</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T19</th>\n",
       "      <td>Abnormal presence of ultra-large von Willebrand fac...</td>\n",
       "      <td>Abnormality of mentalis muscle (HP:3000007)</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T20</th>\n",
       "      <td>Nephroptosis (HP:0011126)</td>\n",
       "      <td>Knee extension contracture (HP:0034672)</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     term 1  \\\n",
       "#                                                             \n",
       "T1                               Osteoma cutis (HP:0025027)   \n",
       "T2     Synostosis involving the 5th metacarpal (HP:0009708)   \n",
       "T3        Bone-in-a-bone appearance of forearm (HP:0003955)   \n",
       "T4                          Psychotic episodes (HP:0000725)   \n",
       "T5                        Periorbital fullness (HP:0000629)   \n",
       "T6                     Hypoplasia of the ovary (HP:0008724)   \n",
       "T7    Abnormality of liposaccharide metabolism (HP:0010968)   \n",
       "T8   Abnormality of the joint spaces of the elbow (HP:00...   \n",
       "T9        Fatigable weakness of bulbar muscles (HP:0030192)   \n",
       "T10                                    Chordee (HP:0000041)   \n",
       "T11  Increased circulating lactate concentration (HP:000...   \n",
       "T12        Absent middle phalanx of 4th finger (HP:0009294)   \n",
       "T13    Medially fused radial epiphyseal plates (HP:0004013)   \n",
       "T14                    Fibrous tissue neoplasm (HP:0012316)   \n",
       "T15                 Chiari type I malformation (HP:0007099)   \n",
       "T16                   Adrenal pheochromocytoma (HP:0006748)   \n",
       "T17                  Unilateral radial aplasia (HP:0011908)   \n",
       "T18                           Attached earlobe (HP:0009907)   \n",
       "T19  Abnormal presence of ultra-large von Willebrand fac...   \n",
       "T20                               Nephroptosis (HP:0011126)   \n",
       "\n",
       "                                                     term 2  text  IC-Jaccard  \\\n",
       "#                                                                               \n",
       "T1   Patchy sclerosis of the proximal phalanx of the 3rd...  0.49        0.01   \n",
       "T2       Proximal radioulnar joint dislocation (HP:6000890)  0.57        0.04   \n",
       "T3   Bullet-shaped middle phalanx of the 3rd finger (HP:...  0.58        0.06   \n",
       "T4   Focal aware emotional seizure with laughing (HP:003...  0.58        0.06   \n",
       "T5                          Lower eyelid edema (HP:0012568)  0.84        0.16   \n",
       "T6             Increased antral follicle count (HP:0033086)  0.58        0.08   \n",
       "T7              Abnormality of abdominal situs (HP:0011620)  0.45        0.02   \n",
       "T8   Irregular epiphysis of the proximal phalanx of the ...  0.54        0.07   \n",
       "T9                  Cervical motion tenderness (HP:6000107)  0.38        0.01   \n",
       "T10      Vesicoallantoic abdominal wall defect (HP:0030733)  0.45        0.04   \n",
       "T11  Elevated urinary 5-amino-4-imidazolecarboxamide-rib...  0.46        0.05   \n",
       "T12  Patchy sclerosis of the middle phalanx of the 4th f...  0.78        0.19   \n",
       "T13  Abnormal renal corticomedullary differentiation (HP...  0.34        0.01   \n",
       "T14          Neoplasm of the parathyroid gland (HP:0100733)  0.57        0.09   \n",
       "T15                Abdominal aortic dissection (HP:0033311)  0.35        0.01   \n",
       "T16                       Uterine adenofibroma (HP:0034709)  0.46        0.05   \n",
       "T17              Primary vesicoureteral reflux (HP:0033738)  0.32        0.01   \n",
       "T18       Splayed superior cerebellar peduncle (HP:0030285)  0.34        0.01   \n",
       "T19             Abnormality of mentalis muscle (HP:3000007)  0.34        0.01   \n",
       "T20                 Knee extension contracture (HP:0034672)  0.36        0.01   \n",
       "\n",
       "     Jaccard is_a+part_of  Jaccard is_a  \\\n",
       "#                                         \n",
       "T1                   0.05          0.05   \n",
       "T2                   0.13          0.13   \n",
       "T3                   0.17          0.17   \n",
       "T4                   0.22          0.22   \n",
       "T5                   0.30          0.30   \n",
       "T6                   0.24          0.24   \n",
       "T7                   0.20          0.20   \n",
       "T8                   0.18          0.18   \n",
       "T9                   0.12          0.12   \n",
       "T10                  0.18          0.18   \n",
       "T11                  0.20          0.20   \n",
       "T12                  0.33          0.33   \n",
       "T13                  0.07          0.07   \n",
       "T14                  0.27          0.27   \n",
       "T15                  0.10          0.10   \n",
       "T16                  0.12          0.12   \n",
       "T17                  0.06          0.06   \n",
       "T18                  0.10          0.10   \n",
       "T19                  0.10          0.10   \n",
       "T20                  0.10          0.10   \n",
       "\n",
       "                                                    MICA  \n",
       "#                                                         \n",
       "T1                                Phenotypic abnormality  \n",
       "T2                         Abnormality of the upper limb  \n",
       "T3                         Abnormality of the upper limb  \n",
       "T4                    Abnormal nervous system physiology  \n",
       "T5                 Abnormality of the periorbital region  \n",
       "T6                     Abnormality of the genital system  \n",
       "T7                                Phenotypic abnormality  \n",
       "T8                         Abnormality of the upper limb  \n",
       "T9                                Phenotypic abnormality  \n",
       "T10              Abnormality of the genitourinary system  \n",
       "T11                Abnormality of metabolism/homeostasis  \n",
       "T12  Abnormality of the middle phalanx of the 4th finger  \n",
       "T13                               Phenotypic abnormality  \n",
       "T14                                             Neoplasm  \n",
       "T15                               Phenotypic abnormality  \n",
       "T16                          Neoplasm by anatomical site  \n",
       "T17                               Phenotypic abnormality  \n",
       "T18                               Phenotypic abnormality  \n",
       "T19                               Phenotypic abnormality  \n",
       "T20                               Phenotypic abnormality  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### MONDO: ontology-similar, text-dissimilar"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term 1</th>\n",
       "      <th>term 2</th>\n",
       "      <th>text</th>\n",
       "      <th>IC-Jaccard</th>\n",
       "      <th>Jaccard is_a+part_of</th>\n",
       "      <th>Jaccard is_a</th>\n",
       "      <th>MICA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O1</th>\n",
       "      <td>dilated cardiomyopathy 2A (MONDO:0012746)</td>\n",
       "      <td>46,XY sex reversal 6 (MONDO:0013410)</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>familial cardiomyopathy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O2</th>\n",
       "      <td>monosodium glutamate sensitivity (MONDO:0009280)</td>\n",
       "      <td>Lamb-Shaffer syndrome (MONDO:0014778)</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.83</td>\n",
       "      <td>hereditary disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>autism, susceptibility to, 20 (MONDO:0030004)</td>\n",
       "      <td>gallbladder disease 4 (MONDO:1010151)</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>inherited disease susceptibility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O4</th>\n",
       "      <td>trigonocephaly (MONDO:0000156)</td>\n",
       "      <td>osteogenesis imperfecta, type 20 (MONDO:0032846)</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>bone development disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O5</th>\n",
       "      <td>fat necrosis of breast (MONDO:0001101)</td>\n",
       "      <td>intellectual disability, anterior maxillary protrus...</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>human disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O6</th>\n",
       "      <td>maleylacetoacetate isomerase deficiency (MONDO:0060...</td>\n",
       "      <td>immunodeficiency 109 with lymphoproliferation (MOND...</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>hereditary disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O7</th>\n",
       "      <td>fire ant poisoning (MONDO:0100341)</td>\n",
       "      <td>OFD1-related ciliopathy (MONDO:1040039)</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>disease by etiologic mechanism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O8</th>\n",
       "      <td>fumaric aciduria (MONDO:0011730)</td>\n",
       "      <td>Leber-like hereditary optic neuropathy, autosomal r...</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>inborn mitochondrial metabolism disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O9</th>\n",
       "      <td>neonatal abstinence syndrome (MONDO:0005566)</td>\n",
       "      <td>ectodermal dysplasia 13, hair/tooth type (MONDO:004...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>syndromic disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O10</th>\n",
       "      <td>portosystemic shunt, non-human animal (MONDO:1011409)</td>\n",
       "      <td>XX difference of sexual development, dog (MONDO:101...</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>disorder of development or morphogenesis, non-human...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O11</th>\n",
       "      <td>patent ductus venosus (MONDO:0011089)</td>\n",
       "      <td>hypertrophic cardiomyopathy 16 (MONDO:0013455)</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>cardiovascular disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O12</th>\n",
       "      <td>Huntington disease, non-human animal (MONDO:1010231)</td>\n",
       "      <td>acute equine grass sickness (MONDO:1016381)</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>central nervous system disorder, non-human animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O13</th>\n",
       "      <td>infectious ectromelia (MONDO:0005809)</td>\n",
       "      <td>ascarid impaction, non-human animal (MONDO:1014847)</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>infectious disease, non-human animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O14</th>\n",
       "      <td>otosclerosis 8 (MONDO:0012797)</td>\n",
       "      <td>Giacheti syndrome (MONDO:0013037)</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>hereditary disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O15</th>\n",
       "      <td>oculocutaneous albinism, OCA2-related, Mexican tetr...</td>\n",
       "      <td>intestinal malabsorption, non-human animal (MONDO:1...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>disease by body system or component, non-human animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O16</th>\n",
       "      <td>immunoglobulin M deficiency, horse (MONDO:1012135)</td>\n",
       "      <td>spermatic cord varicoele, non-human animal (MONDO:1...</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>disease by body system or component, non-human animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O17</th>\n",
       "      <td>moyamoya angiopathy-short stature-facial dysmorphis...</td>\n",
       "      <td>aneurysm, intracranial berry, 9 (MONDO:0012949)</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>cerebral arterial disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O18</th>\n",
       "      <td>spinocerebellar degeneration with slow eye movement...</td>\n",
       "      <td>Alzheimer disease 5 (MONDO:0011194)</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>hereditary dementia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O19</th>\n",
       "      <td>gastrointestinal tuberculosis (MONDO:0005768)</td>\n",
       "      <td>nail infection (MONDO:0024487)</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>infectious disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O20</th>\n",
       "      <td>onychodystrophy, non-human animal (MONDO:1017354)</td>\n",
       "      <td>Appaloosa horse parentage syndrome (MONDO:1017535)</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.83</td>\n",
       "      <td>skin disorder, non-human animal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     term 1  \\\n",
       "#                                                             \n",
       "O1                dilated cardiomyopathy 2A (MONDO:0012746)   \n",
       "O2         monosodium glutamate sensitivity (MONDO:0009280)   \n",
       "O3            autism, susceptibility to, 20 (MONDO:0030004)   \n",
       "O4                           trigonocephaly (MONDO:0000156)   \n",
       "O5                   fat necrosis of breast (MONDO:0001101)   \n",
       "O6   maleylacetoacetate isomerase deficiency (MONDO:0060...   \n",
       "O7                       fire ant poisoning (MONDO:0100341)   \n",
       "O8                         fumaric aciduria (MONDO:0011730)   \n",
       "O9             neonatal abstinence syndrome (MONDO:0005566)   \n",
       "O10   portosystemic shunt, non-human animal (MONDO:1011409)   \n",
       "O11                   patent ductus venosus (MONDO:0011089)   \n",
       "O12    Huntington disease, non-human animal (MONDO:1010231)   \n",
       "O13                   infectious ectromelia (MONDO:0005809)   \n",
       "O14                          otosclerosis 8 (MONDO:0012797)   \n",
       "O15  oculocutaneous albinism, OCA2-related, Mexican tetr...   \n",
       "O16      immunoglobulin M deficiency, horse (MONDO:1012135)   \n",
       "O17  moyamoya angiopathy-short stature-facial dysmorphis...   \n",
       "O18  spinocerebellar degeneration with slow eye movement...   \n",
       "O19           gastrointestinal tuberculosis (MONDO:0005768)   \n",
       "O20       onychodystrophy, non-human animal (MONDO:1017354)   \n",
       "\n",
       "                                                     term 2  text  IC-Jaccard  \\\n",
       "#                                                                               \n",
       "O1                     46,XY sex reversal 6 (MONDO:0013410)  0.10        0.28   \n",
       "O2                    Lamb-Shaffer syndrome (MONDO:0014778)  0.14        0.29   \n",
       "O3                    gallbladder disease 4 (MONDO:1010151)  0.14        0.28   \n",
       "O4         osteogenesis imperfecta, type 20 (MONDO:0032846)  0.19        0.35   \n",
       "O5   intellectual disability, anterior maxillary protrus...  0.04        0.12   \n",
       "O6   immunodeficiency 109 with lymphoproliferation (MOND...  0.16        0.18   \n",
       "O7                  OFD1-related ciliopathy (MONDO:1040039)  0.04        0.11   \n",
       "O8   Leber-like hereditary optic neuropathy, autosomal r...  0.23        0.31   \n",
       "O9   ectodermal dysplasia 13, hair/tooth type (MONDO:004...  0.03        0.11   \n",
       "O10  XX difference of sexual development, dog (MONDO:101...  0.20        0.23   \n",
       "O11          hypertrophic cardiomyopathy 16 (MONDO:0013455)  0.16        0.14   \n",
       "O12             acute equine grass sickness (MONDO:1016381)  0.20        0.20   \n",
       "O13     ascarid impaction, non-human animal (MONDO:1014847)  0.24        0.34   \n",
       "O14                       Giacheti syndrome (MONDO:0013037)  0.11        0.12   \n",
       "O15  intestinal malabsorption, non-human animal (MONDO:1...  0.13        0.13   \n",
       "O16  spermatic cord varicoele, non-human animal (MONDO:1...  0.16        0.14   \n",
       "O17         aneurysm, intracranial berry, 9 (MONDO:0012949)  0.26        0.45   \n",
       "O18                     Alzheimer disease 5 (MONDO:0011194)  0.23        0.30   \n",
       "O19                          nail infection (MONDO:0024487)  0.18        0.17   \n",
       "O20      Appaloosa horse parentage syndrome (MONDO:1017535)  0.26        0.45   \n",
       "\n",
       "     Jaccard is_a+part_of  Jaccard is_a  \\\n",
       "#                                         \n",
       "O1                   0.50          0.50   \n",
       "O2                   0.83          0.83   \n",
       "O3                   0.70          0.70   \n",
       "O4                   0.61          0.61   \n",
       "O5                   0.50          0.50   \n",
       "O6                   0.62          0.62   \n",
       "O7                   0.47          0.47   \n",
       "O8                   0.54          0.54   \n",
       "O9                   0.39          0.39   \n",
       "O10                  0.56          0.56   \n",
       "O11                  0.44          0.44   \n",
       "O12                  0.50          0.50   \n",
       "O13                  0.71          0.71   \n",
       "O14                  0.50          0.50   \n",
       "O15                  0.47          0.47   \n",
       "O16                  0.57          0.57   \n",
       "O17                  0.66          0.66   \n",
       "O18                  0.53          0.53   \n",
       "O19                  0.50          0.50   \n",
       "O20                  0.83          0.83   \n",
       "\n",
       "                                                       MICA  \n",
       "#                                                            \n",
       "O1                                  familial cardiomyopathy  \n",
       "O2                                       hereditary disease  \n",
       "O3                         inherited disease susceptibility  \n",
       "O4                                 bone development disease  \n",
       "O5                                            human disease  \n",
       "O6                                       hereditary disease  \n",
       "O7                           disease by etiologic mechanism  \n",
       "O8                 inborn mitochondrial metabolism disorder  \n",
       "O9                                        syndromic disease  \n",
       "O10  disorder of development or morphogenesis, non-human...  \n",
       "O11                                 cardiovascular disorder  \n",
       "O12       central nervous system disorder, non-human animal  \n",
       "O13                    infectious disease, non-human animal  \n",
       "O14                                      hereditary disease  \n",
       "O15   disease by body system or component, non-human animal  \n",
       "O16   disease by body system or component, non-human animal  \n",
       "O17                               cerebral arterial disease  \n",
       "O18                                     hereditary dementia  \n",
       "O19                                      infectious disease  \n",
       "O20                         skin disorder, non-human animal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### MONDO: text-similar, ontology-dissimilar"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term 1</th>\n",
       "      <th>term 2</th>\n",
       "      <th>text</th>\n",
       "      <th>IC-Jaccard</th>\n",
       "      <th>Jaccard is_a+part_of</th>\n",
       "      <th>Jaccard is_a</th>\n",
       "      <th>MICA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T1</th>\n",
       "      <td>endobronchial lipoma (MONDO:0000961)</td>\n",
       "      <td>lipid-rich breast carcinoma (MONDO:0021090)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>autosomal dominant nocturnal frontal lobe epilepsy ...</td>\n",
       "      <td>subependymal giant cell astrocytoma (MONDO:0016693)</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>nervous system disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>pyloric stenosis, infantile hypertrophic, 1 (MONDO:...</td>\n",
       "      <td>pseudotumor cerebri (MONDO:0009468)</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>disease by body system or component</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>late congenital syphilis (MONDO:0005821)</td>\n",
       "      <td>short stature, intellectual disability, callosal ag...</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>disease by etiologic mechanism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>parapharyngeal meningioma (MONDO:0004502)</td>\n",
       "      <td>benign neoplasm of epiglottis (MONDO:0021446)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>silent sinus syndrome (MONDO:0019108)</td>\n",
       "      <td>PNPLA6-related spastic paraplegia with or without a...</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>disease by body system or component</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>ampulla of vater mucinous adenocarcinoma (MONDO:000...</td>\n",
       "      <td>diffuse cavernous hemangioma of the rectum (MONDO:0...</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>intestinal neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>sphenoidal sinus cancer (MONDO:0001994)</td>\n",
       "      <td>acquired laryngomalacia (MONDO:0850295)</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>respiratory system disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>episodic ataxia type 2 (MONDO:0007163)</td>\n",
       "      <td>combined deficiency of factor V and factor VIII (MO...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>disease by body system or component</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10</th>\n",
       "      <td>retinal hemangioblastoma (MONDO:0003343)</td>\n",
       "      <td>breast papillary carcinoma (MONDO:0003532)</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T11</th>\n",
       "      <td>osteoporosis-pseudoglioma syndrome (MONDO:0009820)</td>\n",
       "      <td>limb-girdle muscular dystrophy (MONDO:0016971)</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>musculoskeletal system disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T12</th>\n",
       "      <td>gastric linitis plastica (MONDO:0018184)</td>\n",
       "      <td>supratip dysplasia (MONDO:0018757)</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>disease by body system or component</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T13</th>\n",
       "      <td>epulis (MONDO:0003396)</td>\n",
       "      <td>benign peripheral nerve granular cell tumor (MONDO:...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>cancer or benign tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T14</th>\n",
       "      <td>malignant thyroid stimulating hormone producing neo...</td>\n",
       "      <td>functional pancreatic neuroendocrine tumor (MONDO:0...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>functioning endocrine neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T15</th>\n",
       "      <td>benign mammary dysplasia (MONDO:0004808)</td>\n",
       "      <td>trichoepithelioma, multiple familial, 2 (MONDO:0012...</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>disease by body system or component</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T16</th>\n",
       "      <td>nephrolithiasis, X-linked recessive, with renal fai...</td>\n",
       "      <td>tapetal degeneration, dog (MONDO:1012263)</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T17</th>\n",
       "      <td>tympanosclerosis (MONDO:0001443)</td>\n",
       "      <td>laryngeal papillomatosis (MONDO:0023597)</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>otorhinolaryngologic disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T18</th>\n",
       "      <td>pontocerebellar hypoplasia type 2C (MONDO:0012891)</td>\n",
       "      <td>noonan syndrome 12 (MONDO:0032839)</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>developmental defect during embryogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T19</th>\n",
       "      <td>neurofibroma of the esophagus (MONDO:0004837)</td>\n",
       "      <td>porokeratotic eccrine ostial and dermal duct nevus ...</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T20</th>\n",
       "      <td>autoimmune thrombocytopenic purpura, dog (MONDO:101...</td>\n",
       "      <td>myositis ossificans, pig (MONDO:1010920)</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>disease by body system or component, non-human animal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     term 1  \\\n",
       "#                                                             \n",
       "T1                     endobronchial lipoma (MONDO:0000961)   \n",
       "T2   autosomal dominant nocturnal frontal lobe epilepsy ...   \n",
       "T3   pyloric stenosis, infantile hypertrophic, 1 (MONDO:...   \n",
       "T4                 late congenital syphilis (MONDO:0005821)   \n",
       "T5                parapharyngeal meningioma (MONDO:0004502)   \n",
       "T6                    silent sinus syndrome (MONDO:0019108)   \n",
       "T7   ampulla of vater mucinous adenocarcinoma (MONDO:000...   \n",
       "T8                  sphenoidal sinus cancer (MONDO:0001994)   \n",
       "T9                   episodic ataxia type 2 (MONDO:0007163)   \n",
       "T10                retinal hemangioblastoma (MONDO:0003343)   \n",
       "T11      osteoporosis-pseudoglioma syndrome (MONDO:0009820)   \n",
       "T12                gastric linitis plastica (MONDO:0018184)   \n",
       "T13                                  epulis (MONDO:0003396)   \n",
       "T14  malignant thyroid stimulating hormone producing neo...   \n",
       "T15                benign mammary dysplasia (MONDO:0004808)   \n",
       "T16  nephrolithiasis, X-linked recessive, with renal fai...   \n",
       "T17                        tympanosclerosis (MONDO:0001443)   \n",
       "T18      pontocerebellar hypoplasia type 2C (MONDO:0012891)   \n",
       "T19           neurofibroma of the esophagus (MONDO:0004837)   \n",
       "T20  autoimmune thrombocytopenic purpura, dog (MONDO:101...   \n",
       "\n",
       "                                                     term 2  text  IC-Jaccard  \\\n",
       "#                                                                               \n",
       "T1              lipid-rich breast carcinoma (MONDO:0021090)  0.50        0.07   \n",
       "T2      subependymal giant cell astrocytoma (MONDO:0016693)  0.34        0.04   \n",
       "T3                      pseudotumor cerebri (MONDO:0009468)  0.35        0.05   \n",
       "T4   short stature, intellectual disability, callosal ag...  0.34        0.05   \n",
       "T5            benign neoplasm of epiglottis (MONDO:0021446)  0.50        0.08   \n",
       "T6   PNPLA6-related spastic paraplegia with or without a...  0.31        0.04   \n",
       "T7   diffuse cavernous hemangioma of the rectum (MONDO:0...  0.42        0.07   \n",
       "T8                  acquired laryngomalacia (MONDO:0850295)  0.31        0.04   \n",
       "T9   combined deficiency of factor V and factor VIII (MO...  0.33        0.05   \n",
       "T10              breast papillary carcinoma (MONDO:0003532)  0.32        0.05   \n",
       "T11          limb-girdle muscular dystrophy (MONDO:0016971)  0.32        0.05   \n",
       "T12                      supratip dysplasia (MONDO:0018757)  0.29        0.04   \n",
       "T13  benign peripheral nerve granular cell tumor (MONDO:...  0.33        0.06   \n",
       "T14  functional pancreatic neuroendocrine tumor (MONDO:0...  0.55        0.10   \n",
       "T15  trichoepithelioma, multiple familial, 2 (MONDO:0012...  0.34        0.06   \n",
       "T16               tapetal degeneration, dog (MONDO:1012263)  0.27        0.04   \n",
       "T17                laryngeal papillomatosis (MONDO:0023597)  0.37        0.07   \n",
       "T18                      noonan syndrome 12 (MONDO:0032839)  0.39        0.07   \n",
       "T19  porokeratotic eccrine ostial and dermal duct nevus ...  0.31        0.06   \n",
       "T20                myositis ossificans, pig (MONDO:1010920)  0.32        0.06   \n",
       "\n",
       "     Jaccard is_a+part_of  Jaccard is_a  \\\n",
       "#                                         \n",
       "T1                   0.27          0.27   \n",
       "T2                   0.23          0.23   \n",
       "T3                   0.28          0.28   \n",
       "T4                   0.28          0.28   \n",
       "T5                   0.32          0.32   \n",
       "T6                   0.24          0.24   \n",
       "T7                   0.19          0.19   \n",
       "T8                   0.18          0.18   \n",
       "T9                   0.30          0.30   \n",
       "T10                  0.20          0.20   \n",
       "T11                  0.19          0.19   \n",
       "T12                  0.25          0.25   \n",
       "T13                  0.27          0.27   \n",
       "T14                  0.25          0.25   \n",
       "T15                  0.31          0.31   \n",
       "T16                  0.26          0.26   \n",
       "T17                  0.26          0.26   \n",
       "T18                  0.25          0.25   \n",
       "T19                  0.24          0.24   \n",
       "T20                  0.32          0.32   \n",
       "\n",
       "                                                      MICA  \n",
       "#                                                           \n",
       "T1                                                neoplasm  \n",
       "T2                                 nervous system disorder  \n",
       "T3                     disease by body system or component  \n",
       "T4                          disease by etiologic mechanism  \n",
       "T5                                                neoplasm  \n",
       "T6                     disease by body system or component  \n",
       "T7                                     intestinal neoplasm  \n",
       "T8                             respiratory system disorder  \n",
       "T9                     disease by body system or component  \n",
       "T10                                               neoplasm  \n",
       "T11                        musculoskeletal system disorder  \n",
       "T12                    disease by body system or component  \n",
       "T13                                 cancer or benign tumor  \n",
       "T14                         functioning endocrine neoplasm  \n",
       "T15                    disease by body system or component  \n",
       "T16                                                disease  \n",
       "T17                           otorhinolaryngologic disease  \n",
       "T18              developmental defect during embryogenesis  \n",
       "T19                                               neoplasm  \n",
       "T20  disease by body system or component, non-human animal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### GO: ontology-similar, text-dissimilar"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term 1</th>\n",
       "      <th>term 2</th>\n",
       "      <th>text</th>\n",
       "      <th>IC-Jaccard</th>\n",
       "      <th>Jaccard is_a+part_of</th>\n",
       "      <th>Jaccard is_a</th>\n",
       "      <th>MICA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O1</th>\n",
       "      <td>ground meristem histogenesis (GO:0010066)</td>\n",
       "      <td>style development (GO:0048479)</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.62</td>\n",
       "      <td>reproductive structure development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O2</th>\n",
       "      <td>catecholamine uptake (GO:0090493)</td>\n",
       "      <td>migracytosis (GO:0140495)</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>RecFOR complex (GO:0043850)</td>\n",
       "      <td>Hpa2 acetyltransferase complex (GO:1990331)</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.38</td>\n",
       "      <td>chromosome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O4</th>\n",
       "      <td>phosphatidylinositol 3-kinase complex, class III (G...</td>\n",
       "      <td>pellicular membrane (GO:0120267)</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.21</td>\n",
       "      <td>membrane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O5</th>\n",
       "      <td>telomerase holoenzyme complex (GO:0005697)</td>\n",
       "      <td>repressor ecdysone receptor complex (GO:0008231)</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.50</td>\n",
       "      <td>nuclear protein-containing complex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O6</th>\n",
       "      <td>meiotic spindle pole (GO:0090619)</td>\n",
       "      <td>vascular endothelial glycocalyx (GO:0120239)</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.62</td>\n",
       "      <td>cellular anatomical structure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O7</th>\n",
       "      <td>viral integration complex (GO:0019035)</td>\n",
       "      <td>endospore cortex (GO:0043595)</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.33</td>\n",
       "      <td>cellular anatomical structure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O8</th>\n",
       "      <td>protein-phosphoribosyl dephospho-coenzyme A linkage...</td>\n",
       "      <td>fractalkine production (GO:0032603)</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>macromolecule metabolic process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O9</th>\n",
       "      <td>perikaryon (GO:0043204)</td>\n",
       "      <td>extrahaustorial matrix (GO:0085036)</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.80</td>\n",
       "      <td>cellular anatomical structure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O10</th>\n",
       "      <td>L-galactose dehydrogenase activity (GO:0010349)</td>\n",
       "      <td>prostaglandin F synthase activity (GO:0047017)</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>oxidoreductase activity, acting on the CH-OH group ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O11</th>\n",
       "      <td>LRR domain binding (GO:0030275)</td>\n",
       "      <td>V2 vasopressin receptor binding (GO:0031896)</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>protein binding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O12</th>\n",
       "      <td>positive regulation of interferon-beta production (...</td>\n",
       "      <td>RNA destabilization (GO:0050779)</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>positive regulation of macromolecule metabolic process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O13</th>\n",
       "      <td>lymphatic endothelial cell differentiation (GO:0060...</td>\n",
       "      <td>superior temporal gyrus development (GO:0071109)</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.31</td>\n",
       "      <td>system development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O14</th>\n",
       "      <td>selenocysteine incorporation (GO:0001514)</td>\n",
       "      <td>regulation of translation at postsynapse, modulatin...</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.47</td>\n",
       "      <td>regulation of translation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O15</th>\n",
       "      <td>potassium:proton exchanging ATPase complex (GO:0005...</td>\n",
       "      <td>HFE-transferrin receptor complex (GO:1990712)</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.50</td>\n",
       "      <td>plasma membrane protein complex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O16</th>\n",
       "      <td>late endosome (GO:0005770)</td>\n",
       "      <td>fusiform vesicle (GO:0120002)</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.83</td>\n",
       "      <td>cytoplasmic vesicle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O17</th>\n",
       "      <td>secondary cell wall (GO:0009531)</td>\n",
       "      <td>basal ring of apical complex (GO:0020032)</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.62</td>\n",
       "      <td>cellular anatomical structure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O18</th>\n",
       "      <td>L-galactose dehydrogenase activity (GO:0010349)</td>\n",
       "      <td>taxane 13-alpha-hydroxylase activity (GO:0050598)</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>oxidoreductase activity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O19</th>\n",
       "      <td>growth factor receptor binding (GO:0070851)</td>\n",
       "      <td>K6-linked polyubiquitin modification-dependent prot...</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>protein binding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O20</th>\n",
       "      <td>sucrase-isomaltase complex (GO:0070014)</td>\n",
       "      <td>7SK snRNP (GO:0120259)</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.38</td>\n",
       "      <td>protein-containing complex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     term 1  \\\n",
       "#                                                             \n",
       "O1                ground meristem histogenesis (GO:0010066)   \n",
       "O2                        catecholamine uptake (GO:0090493)   \n",
       "O3                              RecFOR complex (GO:0043850)   \n",
       "O4   phosphatidylinositol 3-kinase complex, class III (G...   \n",
       "O5               telomerase holoenzyme complex (GO:0005697)   \n",
       "O6                        meiotic spindle pole (GO:0090619)   \n",
       "O7                   viral integration complex (GO:0019035)   \n",
       "O8   protein-phosphoribosyl dephospho-coenzyme A linkage...   \n",
       "O9                                  perikaryon (GO:0043204)   \n",
       "O10         L-galactose dehydrogenase activity (GO:0010349)   \n",
       "O11                         LRR domain binding (GO:0030275)   \n",
       "O12  positive regulation of interferon-beta production (...   \n",
       "O13  lymphatic endothelial cell differentiation (GO:0060...   \n",
       "O14               selenocysteine incorporation (GO:0001514)   \n",
       "O15  potassium:proton exchanging ATPase complex (GO:0005...   \n",
       "O16                              late endosome (GO:0005770)   \n",
       "O17                        secondary cell wall (GO:0009531)   \n",
       "O18         L-galactose dehydrogenase activity (GO:0010349)   \n",
       "O19             growth factor receptor binding (GO:0070851)   \n",
       "O20                 sucrase-isomaltase complex (GO:0070014)   \n",
       "\n",
       "                                                     term 2  text  IC-Jaccard  \\\n",
       "#                                                                               \n",
       "O1                           style development (GO:0048479)  0.15        0.27   \n",
       "O2                                migracytosis (GO:0140495)  0.14        0.25   \n",
       "O3              Hpa2 acetyltransferase complex (GO:1990331)  0.19        0.37   \n",
       "O4                         pellicular membrane (GO:0120267)  0.08        0.17   \n",
       "O5         repressor ecdysone receptor complex (GO:0008231)  0.27        0.49   \n",
       "O6             vascular endothelial glycocalyx (GO:0120239)  0.06        0.12   \n",
       "O7                            endospore cortex (GO:0043595)  0.12        0.14   \n",
       "O8                      fractalkine production (GO:0032603)  0.08        0.12   \n",
       "O9                      extrahaustorial matrix (GO:0085036)  0.09        0.11   \n",
       "O10          prostaglandin F synthase activity (GO:0047017)  0.33        0.48   \n",
       "O11            V2 vasopressin receptor binding (GO:0031896)  0.20        0.20   \n",
       "O12                        RNA destabilization (GO:0050779)  0.25        0.25   \n",
       "O13        superior temporal gyrus development (GO:0071109)  0.15        0.14   \n",
       "O14  regulation of translation at postsynapse, modulatin...  0.26        0.26   \n",
       "O15           HFE-transferrin receptor complex (GO:1990712)  0.33        0.42   \n",
       "O16                           fusiform vesicle (GO:0120002)  0.37        0.65   \n",
       "O17               basal ring of apical complex (GO:0020032)  0.22        0.18   \n",
       "O18       taxane 13-alpha-hydroxylase activity (GO:0050598)  0.23        0.17   \n",
       "O19  K6-linked polyubiquitin modification-dependent prot...  0.27        0.22   \n",
       "O20                                  7SK snRNP (GO:0120259)  0.23        0.16   \n",
       "\n",
       "     Jaccard is_a+part_of  Jaccard is_a  \\\n",
       "#                                         \n",
       "O1                   0.45          0.62   \n",
       "O2                   0.50          0.50   \n",
       "O3                   0.61          0.38   \n",
       "O4                   0.38          0.21   \n",
       "O5                   0.72          0.50   \n",
       "O6                   0.33          0.62   \n",
       "O7                   0.40          0.33   \n",
       "O8                   0.32          0.32   \n",
       "O9                   0.29          0.80   \n",
       "O10                  0.78          0.78   \n",
       "O11                  0.45          0.45   \n",
       "O12                  0.42          0.42   \n",
       "O13                  0.29          0.31   \n",
       "O14                  0.41          0.47   \n",
       "O15                  0.65          0.50   \n",
       "O16                  0.82          0.83   \n",
       "O17                  0.47          0.62   \n",
       "O18                  0.42          0.42   \n",
       "O19                  0.50          0.50   \n",
       "O20                  0.33          0.38   \n",
       "\n",
       "                                                       MICA  \n",
       "#                                                            \n",
       "O1                       reproductive structure development  \n",
       "O2                                                transport  \n",
       "O3                                               chromosome  \n",
       "O4                                                 membrane  \n",
       "O5                       nuclear protein-containing complex  \n",
       "O6                            cellular anatomical structure  \n",
       "O7                            cellular anatomical structure  \n",
       "O8                          macromolecule metabolic process  \n",
       "O9                            cellular anatomical structure  \n",
       "O10  oxidoreductase activity, acting on the CH-OH group ...  \n",
       "O11                                         protein binding  \n",
       "O12  positive regulation of macromolecule metabolic process  \n",
       "O13                                      system development  \n",
       "O14                               regulation of translation  \n",
       "O15                         plasma membrane protein complex  \n",
       "O16                                     cytoplasmic vesicle  \n",
       "O17                           cellular anatomical structure  \n",
       "O18                                 oxidoreductase activity  \n",
       "O19                                         protein binding  \n",
       "O20                              protein-containing complex  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### GO: text-similar, ontology-dissimilar"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
     },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term 1</th>\n",
       "      <th>term 2</th>\n",
       "      <th>text</th>\n",
       "      <th>IC-Jaccard</th>\n",
       "      <th>Jaccard is_a+part_of</th>\n",
       "      <th>Jaccard is_a</th>\n",
       "      <th>MICA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T1</th>\n",
       "      <td>integrin alpha9-beta1 complex (GO:0034679)</td>\n",
       "      <td>negative regulation of fibronectin-dependent thymoc...</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>(none)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>negative regulation of dendritic spine development ...</td>\n",
       "      <td>positive regulation of dendritic spine morphogenesi...</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>regulation of dendritic spine development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>establishment or maintenance of cytoskeleton polari...</td>\n",
       "      <td>maintenance of neuroblast polarity (GO:0045201)</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.38</td>\n",
       "      <td>establishment or maintenance of cell polarity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>UDP-N-acetylglucosamine transmembrane transporter a...</td>\n",
       "      <td>2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succ...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.17</td>\n",
       "      <td>molecular_function</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>protein localization to linear element (GO:0036181)</td>\n",
       "      <td>positive regulation of protein localization to pres...</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>biological_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>azurophil granule membrane (GO:0035577)</td>\n",
       "      <td>negative regulation of lysosomal membrane permeabil...</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>(none)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>protein-N(PI)-phosphohistidine-trehalose phosphotra...</td>\n",
       "      <td>galactosamine-6-phosphate isomerase activity (GO:00...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.20</td>\n",
       "      <td>catalytic activity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>inhibitory killer cell immunoglobulin-like receptor...</td>\n",
       "      <td>positive regulation of complement-dependent cytotox...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.26</td>\n",
       "      <td>regulation of cellular process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>isocitrate-homoisocitrate dehydrogenase activity (G...</td>\n",
       "      <td>negative regulation of catalytic activity (GO:0043086)</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10</th>\n",
       "      <td>alcohol O-cinnamoyltransferase activity (GO:0047182)</td>\n",
       "      <td>allyl-alcohol dehydrogenase activity (GO:0047655)</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>catalytic activity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T11</th>\n",
       "      <td>long-chain fatty acid-CoA ligase activity (GO:0004467)</td>\n",
       "      <td>methionine-tRNA ligase activity (GO:0004825)</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>ligase activity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T12</th>\n",
       "      <td>5-methyltetrahydropteroyltriglutamate-homocysteine ...</td>\n",
       "      <td>maleylacetate reductase activity (GO:0018506)</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>catalytic activity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T13</th>\n",
       "      <td>death-inducing signaling complex assembly (GO:0071550)</td>\n",
       "      <td>single-stranded 3'-5' DNA helicase activity (GO:199...</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.08</td>\n",
       "      <td>process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T14</th>\n",
       "      <td>regulation of respiratory burst involved in inflamm...</td>\n",
       "      <td>regulation of protein processing in phagocytic vesi...</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>regulation of metabolic process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T15</th>\n",
       "      <td>negative regulation of non-canonical inflammasome c...</td>\n",
       "      <td>negative regulation of phosphatidylserine biosynthe...</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.26</td>\n",
       "      <td>negative regulation of cellular process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T16</th>\n",
       "      <td>symbiont-mediated disruption of host mitochondrion ...</td>\n",
       "      <td>spore dispersal (GO:0075325)</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>biological_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T17</th>\n",
       "      <td>positive regulation of metula development (GO:0070804)</td>\n",
       "      <td>Mo(VI)-molybdopterin cytosine dinucleotide biosynth...</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>biological_process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T18</th>\n",
       "      <td>positive regulation of DNA endoreduplication (GO:00...</td>\n",
       "      <td>positive regulation of lamellipodium morphogenesis ...</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>positive regulation of cellular process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T19</th>\n",
       "      <td>integrin alpha9-beta1 complex (GO:0034679)</td>\n",
       "      <td>anterograde trans-synaptic signaling (GO:0098916)</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>(none)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T20</th>\n",
       "      <td>positive regulation of protein homooligomerization ...</td>\n",
       "      <td>negative regulation of eating behavior (GO:1903999)</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>regulation of biological process</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     term 1  \\\n",
       "#                                                             \n",
       "T1               integrin alpha9-beta1 complex (GO:0034679)   \n",
       "T2   negative regulation of dendritic spine development ...   \n",
       "T3   establishment or maintenance of cytoskeleton polari...   \n",
       "T4   UDP-N-acetylglucosamine transmembrane transporter a...   \n",
       "T5      protein localization to linear element (GO:0036181)   \n",
       "T6                  azurophil granule membrane (GO:0035577)   \n",
       "T7   protein-N(PI)-phosphohistidine-trehalose phosphotra...   \n",
       "T8   inhibitory killer cell immunoglobulin-like receptor...   \n",
       "T9   isocitrate-homoisocitrate dehydrogenase activity (G...   \n",
       "T10    alcohol O-cinnamoyltransferase activity (GO:0047182)   \n",
       "T11  long-chain fatty acid-CoA ligase activity (GO:0004467)   \n",
       "T12  5-methyltetrahydropteroyltriglutamate-homocysteine ...   \n",
       "T13  death-inducing signaling complex assembly (GO:0071550)   \n",
       "T14  regulation of respiratory burst involved in inflamm...   \n",
       "T15  negative regulation of non-canonical inflammasome c...   \n",
       "T16  symbiont-mediated disruption of host mitochondrion ...   \n",
       "T17  positive regulation of metula development (GO:0070804)   \n",
       "T18  positive regulation of DNA endoreduplication (GO:00...   \n",
       "T19              integrin alpha9-beta1 complex (GO:0034679)   \n",
       "T20  positive regulation of protein homooligomerization ...   \n",
       "\n",
       "                                                     term 2  text  IC-Jaccard  \\\n",
       "#                                                                               \n",
       "T1   negative regulation of fibronectin-dependent thymoc...  0.41        0.00   \n",
       "T2   positive regulation of dendritic spine morphogenesi...  0.83        0.07   \n",
       "T3          maintenance of neuroblast polarity (GO:0045201)  0.74        0.07   \n",
       "T4   2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succ...  0.47        0.02   \n",
       "T5   positive regulation of protein localization to pres...  0.44        0.02   \n",
       "T6   negative regulation of lysosomal membrane permeabil...  0.35        0.00   \n",
       "T7   galactosamine-6-phosphate isomerase activity (GO:00...  0.48        0.04   \n",
       "T8   positive regulation of complement-dependent cytotox...  0.55        0.07   \n",
       "T9   negative regulation of catalytic activity (GO:0043086)  0.43        0.03   \n",
       "T10       allyl-alcohol dehydrogenase activity (GO:0047655)  0.64        0.11   \n",
       "T11            methionine-tRNA ligase activity (GO:0004825)  0.51        0.08   \n",
       "T12           maleylacetate reductase activity (GO:0018506)  0.53        0.09   \n",
       "T13  single-stranded 3'-5' DNA helicase activity (GO:199...  0.27        0.01   \n",
       "T14  regulation of protein processing in phagocytic vesi...  0.51        0.09   \n",
       "T15  negative regulation of phosphatidylserine biosynthe...  0.43        0.05   \n",
       "T16                            spore dispersal (GO:0075325)  0.42        0.05   \n",
       "T17  Mo(VI)-molybdopterin cytosine dinucleotide biosynth...  0.28        0.01   \n",
       "T18  positive regulation of lamellipodium morphogenesis ...  0.44        0.07   \n",
       "T19       anterograde trans-synaptic signaling (GO:0098916)  0.24        0.00   \n",
       "T20     negative regulation of eating behavior (GO:1903999)  0.39        0.05   \n",
       "\n",
       "     Jaccard is_a+part_of  Jaccard is_a  \\\n",
       "#                                         \n",
       "T1                   0.00          0.00   \n",
       "T2                   0.16          0.16   \n",
       "T3                   0.16          0.38   \n",
       "T4                   0.10          0.17   \n",
       "T5                   0.14          0.14   \n",
       "T6                   0.00          0.00   \n",
       "T7                   0.13          0.20   \n",
       "T8                   0.22          0.26   \n",
       "T9                   0.14          0.14   \n",
       "T10                  0.33          0.33   \n",
       "T11                  0.17          0.28   \n",
       "T12                  0.31          0.31   \n",
       "T13                  0.05          0.08   \n",
       "T14                  0.23          0.23   \n",
       "T15                  0.15          0.26   \n",
       "T16                  0.30          0.30   \n",
       "T17                  0.07          0.07   \n",
       "T18                  0.21          0.21   \n",
       "T19                  0.00          0.00   \n",
       "T20                  0.19          0.19   \n",
       "\n",
       "                                              MICA  \n",
       "#                                                   \n",
       "T1                                          (none)  \n",
       "T2       regulation of dendritic spine development  \n",
       "T3   establishment or maintenance of cell polarity  \n",
       "T4                              molecular_function  \n",
       "T5                              biological_process  \n",
       "T6                                          (none)  \n",
       "T7                              catalytic activity  \n",
       "T8                  regulation of cellular process  \n",
       "T9                                         process  \n",
       "T10                             catalytic activity  \n",
       "T11                                ligase activity  \n",
       "T12                             catalytic activity  \n",
       "T13                                        process  \n",
       "T14                regulation of metabolic process  \n",
       "T15        negative regulation of cellular process  \n",
       "T16                             biological_process  \n",
       "T17                             biological_process  \n",
       "T18        positive regulation of cellular process  \n",
       "T19                                         (none)  \n",
       "T20               regulation of biological process  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### CL: ontology-similar, text-dissimilar"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term 1</th>\n",
       "      <th>term 2</th>\n",
       "      <th>text</th>\n",
       "      <th>IC-Jaccard</th>\n",
       "      <th>Jaccard is_a+part_of</th>\n",
       "      <th>Jaccard is_a</th>\n",
       "      <th>MICA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O1</th>\n",
       "      <td>olfactory ensheathing cell (CL:0011028)</td>\n",
       "      <td>Astro-NT NN_1 Slc36a2 astrocyte (Mmus) (CL:4307029)</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.65</td>\n",
       "      <td>glial cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O2</th>\n",
       "      <td>melanophage (CL:0002060)</td>\n",
       "      <td>CD8-alpha-positive plasmacytoid dendritic cell (CL:...</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.68</td>\n",
       "      <td>mononuclear phagocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>cone retinal bipolar cell (CL:0000752)</td>\n",
       "      <td>hypothalamic gonadotropin-releasing hormone neuron ...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>glutamatergic neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O4</th>\n",
       "      <td>superior cervical ganglion VIP neuron (CL:4033106)</td>\n",
       "      <td>STRd D2 StrioMat Hybrid MSN indirect pathway medium...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "      <td>neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O5</th>\n",
       "      <td>pvalb chandelier GABAergic interneuron (CL:4023036)</td>\n",
       "      <td>GPe-NDB-SI LHX6-LHX8-GBX1 GABA GABAergic interneuro...</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.67</td>\n",
       "      <td>GABAergic interneuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O6</th>\n",
       "      <td>M13 retinal ganglion cell (CL:0003046)</td>\n",
       "      <td>untufted pyramidal neuron (CL:4023095)</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.65</td>\n",
       "      <td>central nervous system neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O7</th>\n",
       "      <td>H2 horizontal cell (CL:0004218)</td>\n",
       "      <td>spinal cord medial motor column neuron (CL:2000024)</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.72</td>\n",
       "      <td>central nervous system neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O8</th>\n",
       "      <td>sacral ganglion TH neuron (CL:4033126)</td>\n",
       "      <td>AMY-SLEA-BNST GABA GABAergic interneuron (Primate) ...</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.50</td>\n",
       "      <td>neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O9</th>\n",
       "      <td>totipotent stem cell (CL:0000052)</td>\n",
       "      <td>airway submucosal gland duct basal cell (CL:4033024)</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.64</td>\n",
       "      <td>somatic stem cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O10</th>\n",
       "      <td>Oriens-Lacunosum Moleculare neuron (CL:0020074)</td>\n",
       "      <td>pvalb chandelier GABAergic interneuron (Homo sapien...</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>cerebral cortex GABAergic interneuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O11</th>\n",
       "      <td>thalamic excitatory neuron (CL:4023068)</td>\n",
       "      <td>BF SKOR1 Glut glutamatergic neuron of the basal gan...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.71</td>\n",
       "      <td>glutamatergic neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O12</th>\n",
       "      <td>ciliated cell (CL:0000064)</td>\n",
       "      <td>lumbar ganglion TH/NPY neuron (CL:4033123)</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.39</td>\n",
       "      <td>eukaryotic cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O13</th>\n",
       "      <td>mesangial phagocyte (CL:0000777)</td>\n",
       "      <td>type II NK T cell (CL:0000922)</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.45</td>\n",
       "      <td>mononuclear leukocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O14</th>\n",
       "      <td>M6 retinal ganglion cell (CL:0003035)</td>\n",
       "      <td>cervicothoracic ganglion enkephalin neuron (CL:4033...</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.68</td>\n",
       "      <td>neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O15</th>\n",
       "      <td>anterior lateral line ganglion neuron (CL:0008021)</td>\n",
       "      <td>Astro-TE NN_1 Lncpint astrocyte (Mmus) (CL:4307040)</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.39</td>\n",
       "      <td>neural cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O16</th>\n",
       "      <td>forebrain radial glial cell (CL:0013000)</td>\n",
       "      <td>OEC NN_1 Rasgef1c olfactory ensheathing cell (Mmus)...</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.65</td>\n",
       "      <td>glial cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O17</th>\n",
       "      <td>intraepithelial lymphocyte (CL:0002496)</td>\n",
       "      <td>dural macrophage (CL:4042006)</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.45</td>\n",
       "      <td>mononuclear leukocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O18</th>\n",
       "      <td>endo-epithelial cell of pharyngotympanic part of vi...</td>\n",
       "      <td>Astro-NT NN_2 Fzd2 astrocyte (Mmus) (CL:4307034)</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>eukaryotic cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O19</th>\n",
       "      <td>spinal cord radial glial cell (CL:1000073)</td>\n",
       "      <td>NFOL NN_2 9630013A20Rik newly formed oligodendrocyt...</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>glial cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O20</th>\n",
       "      <td>CNS neuron (sensu Nematoda and Protostomia) (CL:000...</td>\n",
       "      <td>cutaneous/subcutaneous mechanoreceptor cell (CL:000...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.63</td>\n",
       "      <td>neuron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     term 1  \\\n",
       "#                                                             \n",
       "O1                  olfactory ensheathing cell (CL:0011028)   \n",
       "O2                                 melanophage (CL:0002060)   \n",
       "O3                   cone retinal bipolar cell (CL:0000752)   \n",
       "O4       superior cervical ganglion VIP neuron (CL:4033106)   \n",
       "O5      pvalb chandelier GABAergic interneuron (CL:4023036)   \n",
       "O6                   M13 retinal ganglion cell (CL:0003046)   \n",
       "O7                          H2 horizontal cell (CL:0004218)   \n",
       "O8                   sacral ganglion TH neuron (CL:4033126)   \n",
       "O9                        totipotent stem cell (CL:0000052)   \n",
       "O10         Oriens-Lacunosum Moleculare neuron (CL:0020074)   \n",
       "O11                 thalamic excitatory neuron (CL:4023068)   \n",
       "O12                              ciliated cell (CL:0000064)   \n",
       "O13                        mesangial phagocyte (CL:0000777)   \n",
       "O14                   M6 retinal ganglion cell (CL:0003035)   \n",
       "O15      anterior lateral line ganglion neuron (CL:0008021)   \n",
       "O16                forebrain radial glial cell (CL:0013000)   \n",
       "O17                 intraepithelial lymphocyte (CL:0002496)   \n",
       "O18  endo-epithelial cell of pharyngotympanic part of vi...   \n",
       "O19              spinal cord radial glial cell (CL:1000073)   \n",
       "O20  CNS neuron (sensu Nematoda and Protostomia) (CL:000...   \n",
       "\n",
       "                                                     term 2  text  IC-Jaccard  \\\n",
       "#                                                                               \n",
       "O1      Astro-NT NN_1 Slc36a2 astrocyte (Mmus) (CL:4307029)  0.06        0.36   \n",
       "O2   CD8-alpha-positive plasmacytoid dendritic cell (CL:...  0.18        0.41   \n",
       "O3   hypothalamic gonadotropin-releasing hormone neuron ...  0.08        0.23   \n",
       "O4   STRd D2 StrioMat Hybrid MSN indirect pathway medium...  0.15        0.26   \n",
       "O5   GPe-NDB-SI LHX6-LHX8-GBX1 GABA GABAergic interneuro...  0.27        0.53   \n",
       "O6                   untufted pyramidal neuron (CL:4023095)  0.25        0.41   \n",
       "O7      spinal cord medial motor column neuron (CL:2000024)  0.27        0.43   \n",
       "O8   AMY-SLEA-BNST GABA GABAergic interneuron (Primate) ...  0.21        0.28   \n",
       "O9     airway submucosal gland duct basal cell (CL:4033024)  0.14        0.18   \n",
       "O10  pvalb chandelier GABAergic interneuron (Homo sapien...  0.30        0.59   \n",
       "O11  BF SKOR1 Glut glutamatergic neuron of the basal gan...  0.29        0.46   \n",
       "O12              lumbar ganglion TH/NPY neuron (CL:4033123)  0.04        0.12   \n",
       "O13                          type II NK T cell (CL:0000922)  0.24        0.27   \n",
       "O14  cervicothoracic ganglion enkephalin neuron (CL:4033...  0.27        0.40   \n",
       "O15     Astro-TE NN_1 Lncpint astrocyte (Mmus) (CL:4307040)  0.14        0.16   \n",
       "O16  OEC NN_1 Rasgef1c olfactory ensheathing cell (Mmus)...  0.20        0.22   \n",
       "O17                           dural macrophage (CL:4042006)  0.18        0.19   \n",
       "O18        Astro-NT NN_2 Fzd2 astrocyte (Mmus) (CL:4307034)  0.01        0.11   \n",
       "O19  NFOL NN_2 9630013A20Rik newly formed oligodendrocyt...  0.21        0.22   \n",
       "O20  cutaneous/subcutaneous mechanoreceptor cell (CL:000...  0.29        0.38   \n",
       "\n",
       "     Jaccard is_a+part_of  Jaccard is_a                                   MICA  \n",
       "#                                                                               \n",
       "O1                   0.71          0.65                             glial cell  \n",
       "O2                   0.54          0.68                  mononuclear phagocyte  \n",
       "O3                   0.40          0.48                   glutamatergic neuron  \n",
       "O4                   0.59          0.52                                 neuron  \n",
       "O5                   0.76          0.67                  GABAergic interneuron  \n",
       "O6                   0.75          0.65          central nervous system neuron  \n",
       "O7                   0.69          0.72          central nervous system neuron  \n",
       "O8                   0.55          0.50                                 neuron  \n",
       "O9                   0.29          0.64                      somatic stem cell  \n",
       "O10                  0.81          0.77  cerebral cortex GABAergic interneuron  \n",
       "O11                  0.77          0.71                   glutamatergic neuron  \n",
       "O12                  0.32          0.39                        eukaryotic cell  \n",
       "O13                  0.46          0.45                  mononuclear leukocyte  \n",
       "O14                  0.74          0.68                                 neuron  \n",
       "O15                  0.48          0.39                            neural cell  \n",
       "O16                  0.41          0.65                             glial cell  \n",
       "O17                  0.33          0.45                  mononuclear leukocyte  \n",
       "O18                  0.38          0.40                        eukaryotic cell  \n",
       "O19                  0.49          0.50                             glial cell  \n",
       "O20                  0.65          0.63                                 neuron  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### CL: text-similar, ontology-dissimilar"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term 1</th>\n",
       "      <th>term 2</th>\n",
       "      <th>text</th>\n",
       "      <th>IC-Jaccard</th>\n",
       "      <th>Jaccard is_a+part_of</th>\n",
       "      <th>Jaccard is_a</th>\n",
       "      <th>MICA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T1</th>\n",
       "      <td>olfactory receptor cell (CL:0000207)</td>\n",
       "      <td>extraglomerular mesangial cell (CL:0002173)</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.21</td>\n",
       "      <td>eukaryotic cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>malignant cell (CL:0001064)</td>\n",
       "      <td>inner phalangeal cell (CL:0005015)</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.24</td>\n",
       "      <td>cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>epidermal mucus secreting cell (CL:0007019)</td>\n",
       "      <td>hypendymal cell (CL:4023181)</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.33</td>\n",
       "      <td>cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>vagal neural crest cell (CL:0011017)</td>\n",
       "      <td>tuft cell of nasal cavity respiratory epithelium (C...</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.29</td>\n",
       "      <td>cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>somatotropin secreting cell (CL:0000295)</td>\n",
       "      <td>hypothalamic gonadotropin-releasing hormone neuron ...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.26</td>\n",
       "      <td>secretory cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>seminiferous tubule epithelial cell (CL:0002625)</td>\n",
       "      <td>somatic nurse-like cell (CL:4029003)</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.38</td>\n",
       "      <td>cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>epinephrine secreting cell (CL:0000454)</td>\n",
       "      <td>intercalated cell of salivary gland (CL:4052048)</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.33</td>\n",
       "      <td>secretory cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>myoepithelial cell of trachea gland (CL:4033021)</td>\n",
       "      <td>stomach smooth muscle circular layer cell (CL:4047036)</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.35</td>\n",
       "      <td>contractile cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>stratified cuboidal epithelial cell (CL:0000241)</td>\n",
       "      <td>kidney proximal convoluted tubule epithelial cell (...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>columnar/cuboidal epithelial cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10</th>\n",
       "      <td>glandular epithelial cell of endometrium (CL:0009084)</td>\n",
       "      <td>type 1 vestibular sensory cell of stato-acoustic ep...</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.23</td>\n",
       "      <td>epithelium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T11</th>\n",
       "      <td>serous secreting cell (CL:0000313)</td>\n",
       "      <td>endothelial cell of uterus (CL:0009095)</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.46</td>\n",
       "      <td>cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T12</th>\n",
       "      <td>bladder microvascular endothelial cell (CL:2000040)</td>\n",
       "      <td>vascular leptomeningeal cell (Mmus) (CL:4023056)</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.35</td>\n",
       "      <td>tissue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T13</th>\n",
       "      <td>myeloid cell (CL:0000763)</td>\n",
       "      <td>basal cell of epithelium of terminal bronchiole (CL...</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.41</td>\n",
       "      <td>eukaryotic cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T14</th>\n",
       "      <td>female germ cell (CL:0000021)</td>\n",
       "      <td>type I enteroendocrine cell (CL:0002277)</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.28</td>\n",
       "      <td>eukaryotic cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T15</th>\n",
       "      <td>myoblast (CL:0000056)</td>\n",
       "      <td>foreskin fibroblast (CL:1001608)</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.47</td>\n",
       "      <td>eukaryotic cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T16</th>\n",
       "      <td>brown preadipocyte (CL:0002335)</td>\n",
       "      <td>adipose microvascular endothelial cell (CL:2000072)</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.50</td>\n",
       "      <td>connective tissue cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T17</th>\n",
       "      <td>nucleate cell (CL:0002242)</td>\n",
       "      <td>activated skeletal muscle satellite cell (CL:0008016)</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T18</th>\n",
       "      <td>lymph node subcapsular sinus macrophage (CL:0000887)</td>\n",
       "      <td>stromal cell of lamina propria of colon (CL:0009040)</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>tissue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T19</th>\n",
       "      <td>endothelial cell of efferent lymphatic vessel (CL:4...</td>\n",
       "      <td>tuft cell of olfactory epithelium (CL:4052037)</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.30</td>\n",
       "      <td>non-connected functional system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T20</th>\n",
       "      <td>trophoblast giant cell (CL:0002488)</td>\n",
       "      <td>monocytoid B cell (CL:0009114)</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.38</td>\n",
       "      <td>eukaryotic cell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     term 1  \\\n",
       "#                                                             \n",
       "T1                     olfactory receptor cell (CL:0000207)   \n",
       "T2                              malignant cell (CL:0001064)   \n",
       "T3              epidermal mucus secreting cell (CL:0007019)   \n",
       "T4                     vagal neural crest cell (CL:0011017)   \n",
       "T5                 somatotropin secreting cell (CL:0000295)   \n",
       "T6         seminiferous tubule epithelial cell (CL:0002625)   \n",
       "T7                  epinephrine secreting cell (CL:0000454)   \n",
       "T8         myoepithelial cell of trachea gland (CL:4033021)   \n",
       "T9         stratified cuboidal epithelial cell (CL:0000241)   \n",
       "T10   glandular epithelial cell of endometrium (CL:0009084)   \n",
       "T11                      serous secreting cell (CL:0000313)   \n",
       "T12     bladder microvascular endothelial cell (CL:2000040)   \n",
       "T13                               myeloid cell (CL:0000763)   \n",
       "T14                           female germ cell (CL:0000021)   \n",
       "T15                                   myoblast (CL:0000056)   \n",
       "T16                         brown preadipocyte (CL:0002335)   \n",
       "T17                              nucleate cell (CL:0002242)   \n",
       "T18    lymph node subcapsular sinus macrophage (CL:0000887)   \n",
       "T19  endothelial cell of efferent lymphatic vessel (CL:4...   \n",
       "T20                     trophoblast giant cell (CL:0002488)   \n",
       "\n",
       "                                                     term 2  text  IC-Jaccard  \\\n",
       "#                                                                               \n",
       "T1              extraglomerular mesangial cell (CL:0002173)  0.47        0.03   \n",
       "T2                       inner phalangeal cell (CL:0005015)  0.37        0.02   \n",
       "T3                             hypendymal cell (CL:4023181)  0.38        0.03   \n",
       "T4   tuft cell of nasal cavity respiratory epithelium (C...  0.36        0.02   \n",
       "T5   hypothalamic gonadotropin-releasing hormone neuron ...  0.55        0.07   \n",
       "T6                     somatic nurse-like cell (CL:4029003)  0.35        0.02   \n",
       "T7         intercalated cell of salivary gland (CL:4052048)  0.40        0.04   \n",
       "T8   stomach smooth muscle circular layer cell (CL:4047036)  0.49        0.07   \n",
       "T9   kidney proximal convoluted tubule epithelial cell (...  0.48        0.07   \n",
       "T10  type 1 vestibular sensory cell of stato-acoustic ep...  0.37        0.04   \n",
       "T11                 endothelial cell of uterus (CL:0009095)  0.41        0.06   \n",
       "T12        vascular leptomeningeal cell (Mmus) (CL:4023056)  0.38        0.05   \n",
       "T13  basal cell of epithelium of terminal bronchiole (CL...  0.30        0.02   \n",
       "T14                type I enteroendocrine cell (CL:0002277)  0.31        0.03   \n",
       "T15                        foreskin fibroblast (CL:1001608)  0.36        0.05   \n",
       "T16     adipose microvascular endothelial cell (CL:2000072)  0.52        0.10   \n",
       "T17   activated skeletal muscle satellite cell (CL:0008016)  0.36        0.05   \n",
       "T18    stromal cell of lamina propria of colon (CL:0009040)  0.35        0.05   \n",
       "T19          tuft cell of olfactory epithelium (CL:4052037)  0.32        0.04   \n",
       "T20                          monocytoid B cell (CL:0009114)  0.33        0.04   \n",
       "\n",
       "     Jaccard is_a+part_of  Jaccard is_a                               MICA  \n",
       "#                                                                           \n",
       "T1                   0.12          0.21                    eukaryotic cell  \n",
       "T2                   0.08          0.24                               cell  \n",
       "T3                   0.11          0.33                               cell  \n",
       "T4                   0.08          0.29                               cell  \n",
       "T5                   0.19          0.26                     secretory cell  \n",
       "T6                   0.10          0.38                               cell  \n",
       "T7                   0.12          0.33                     secretory cell  \n",
       "T8                   0.20          0.35                   contractile cell  \n",
       "T9                   0.19          0.40  columnar/cuboidal epithelial cell  \n",
       "T10                  0.16          0.23                         epithelium  \n",
       "T11                  0.20          0.46                               cell  \n",
       "T12                  0.18          0.35                             tissue  \n",
       "T13                  0.10          0.41                    eukaryotic cell  \n",
       "T14                  0.11          0.28                    eukaryotic cell  \n",
       "T15                  0.20          0.47                    eukaryotic cell  \n",
       "T16                  0.26          0.50             connective tissue cell  \n",
       "T17                  0.19          0.40                               cell  \n",
       "T18                  0.19          0.27                             tissue  \n",
       "T19                  0.14          0.30    non-connected functional system  \n",
       "T20                  0.18          0.38                    eukaryotic cell  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", 55)\n",
    "pd.set_option(\"display.max_rows\", 50)\n",
    "\n",
    "def corner_table(onto, sub, tag):\n",
    "    ids = set(sub.a) | set(sub.b) | {micas[(r.a, r.b)] for r in sub.itertuples()} - {None}\n",
    "    labels = dict(onto.labels(ids))\n",
    "    out = []\n",
    "    for k, r in enumerate(sub.itertuples(), 1):\n",
    "        mica = micas[(r.a, r.b)]\n",
    "        out.append({\n",
    "            \"#\": f\"{tag}{k}\",\n",
    "            \"term 1\": f\"{labels.get(r.a)} ({r.a})\",\n",
    "            \"term 2\": f\"{labels.get(r.b)} ({r.b})\",\n",
    "            \"text\": round(r.text_sim, 2),\n",
    "            \"IC-Jaccard\": round(r.simgic, 2),\n",
    "            \"Jaccard is_a+part_of\": round(r.jaccard_isa_partof, 2),\n",
    "            \"Jaccard is_a\": round(r.jaccard_isa, 2),\n",
    "            \"MICA\": labels.get(mica, mica) if mica else \"(none)\",\n",
    "        })\n",
    "    return pd.DataFrame(out).set_index(\"#\")\n",
    "\n",
    "for name, (onto_only, text_only) in corners.items():\n",
    "    onto = adapters[name][0]\n",
    "    display(Markdown(f\"### {name.upper()}: ontology-similar, text-dissimilar\"))\n",
    "    display(corner_table(onto, onto_only, \"O\"))\n",
    "    display(Markdown(f\"### {name.upper()}: text-similar, ontology-dissimilar\"))\n",
    "    display(corner_table(onto, text_only, \"T\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a964f8a3",
   "metadata": {},
   "source": [
    "## What the corners show\n",
    "\n",
    "**Choice of ontology measure.**\n",
    "\n",
    "- IC weighting improves agreement with text in every ontology (Spearman ρ: HP 0.75 →\n",
    "  0.79, MONDO 0.63 → 0.73, GO 0.71 → 0.76, CL 0.66 → 0.67). Sharing a generic ancestor\n",
    "  no longer counts for much, which is also how the text models behave.\n",
    "- Adding `part_of` changes nothing for HP and MONDO here (none of the sampled terms gets\n",
    "  extra ancestors from it). It helps GO slightly (0.71 → 0.73), where cellular-component\n",
    "  structure is mostly `part_of`. It hurts CL (0.66 → 0.60), where `part_of` reaches into\n",
    "  anatomy and gives unrelated cells shared ancestors such as *tissue* and *epithelium*.\n",
    "- The corners are fairly stable: 7 to 17 of the top-20 pairs in each corner are also in\n",
    "  the top 20 under plain `is_a` Jaccard. The disagreements are mostly real differences\n",
    "  between ontology and text, not artifacts of one measure. IC weighting lowers the\n",
    "  scores of pairs that share only a broad ancestor, but some remain. For example, several\n",
    "  MONDO pairs are still joined only by *hereditary disease* (O2, O6, O14), because many\n",
    "  MONDO diseases are classified along broad axes only.\n",
    "\n",
    "**HP (Phenotypic abnormality only).** The ontology-similar corner is now mostly\n",
    "groupings by physiology or clinical context that the wording doesn't reveal:\n",
    "\n",
    "- *Light-chain paraproteinemia* / *Increased anti-dust mite IgE antibody level*\n",
    "  (abnormal circulating immunoglobulin concentration);\n",
    "- *Recurrent skin infections* / *Autoimmune encephalitis* (increased inflammatory\n",
    "  response);\n",
    "- *Geophagia* / *Bulimia* (abnormal eating behavior);\n",
    "- *Jaw claudication* / *Absent superficial temporal artery pulse* (abnormal arterial\n",
    "  physiology; both are signs of giant cell arteritis).\n",
    "\n",
    "A few groupings may be worth a curator's look, such as *Hip pain* and *Fish odor* both\n",
    "sitting under *Constitutional symptom* (O7).\n",
    "\n",
    "The text-similar corner includes pairs that look clinically related but share only a\n",
    "broad ancestor. *Periorbital fullness* / *Lower eyelid edema* has a text similarity of\n",
    "0.84 but meets only at *Abnormality of the periorbital region*, a candidate for a missing\n",
    "link. Many of the other text-similar pairs are limb terms matched on bone vocabulary\n",
    "(*phalanx*, *radioulnar*, *forearm*) but describing different kinds of abnormality.\n",
    "Similarly, *Absent middle phalanx of 4th finger* / *Patchy sclerosis of the middle\n",
    "phalanx of the 4th finger* (0.78) share an anatomical site; text weighs that heavily,\n",
    "the ontology weighs the kind of abnormality too.\n",
    "\n",
    "**GO.**\n",
    "\n",
    "- Text failures: opposite polarity (*negative regulation of dendritic spine\n",
    "  development* / *positive regulation of dendritic spine morphogenesis*, 0.83); shared\n",
    "  chemical words across different activity classes (*alcohol O-cinnamoyltransferase* /\n",
    "  *allyl-alcohol dehydrogenase*, 0.64, a transferase vs an oxidoreductase); and the\n",
    "  shared phrasing of regulation terms.\n",
    "- Ontology-only knowledge: *late endosome* / *fusiform vesicle* (both cytoplasmic\n",
    "  vesicles; IC-weighted 0.65, text 0.37), and pairs of oxidoreductases.\n",
    "- Protein complexes still populate the ontology-similar corner (e.g. *RecFOR complex* /\n",
    "  *Hpa2 acetyltransferase complex*, joined via `part_of` *chromosome*). Their names say\n",
    "  little about what they share.\n",
    "\n",
    "**CL.** The ontology-similar corner is dominated by neurons and glia, many with\n",
    "taxonomy-style labels (*Astro-NT NN_1 Slc36a2 astrocyte (Mmus)*, *STRd D2 StrioMat\n",
    "Hybrid MSN …*, *GPe-NDB-SI LHX6-LHX8-GBX1 GABA …*). These are hard to find or match by\n",
    "text and may benefit from more descriptive synonyms. The text-similar corner is mostly\n",
    "shared vocabulary (*secreting*, *epithelial*, *endothelial*) across cells whose closest\n",
    "common ancestor is just *cell*.\n",
    "\n",
    "**Caveats.** The disagreement score is rank-based, so every ontology has a top 20 in each\n",
    "corner even where the disagreement is mild. In MONDO, for example, many text-similar\n",
    "picks have text similarity of only about 0.3. Read the tables together with the scores.\n",
    "IC here is computed from ontology structure, not annotation frequency. Vectors are cached,\n",
    "so changing `MODEL`, `MEASURES` or the sample sizes only fetches new terms."
   ]
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