{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1c23a3e3-332b-4712-a2b8-1788cd14087e",
"metadata": {},
"outputs": [],
"source": [
"%reset -f"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "207f38ab-618d-42fa-accb-f400dfc8be34",
"metadata": {},
"outputs": [],
"source": [
"user = \"HHegde\"\n",
"db = f\"/Users/{user}/.data/oaklib/phenio.db\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2e2ce839-b8cc-4f1a-a931-67b85ba0df4d",
"metadata": {},
"outputs": [],
"source": [
"%reload_ext sql\n",
"%sql sqlite:///{db}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "87025967-554f-4c9d-9967-97ce1e40acf7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * sqlite:////Users/HHegde/.data/oaklib/phenio.db\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"
\n",
" \n",
" \n",
" id | \n",
" subject | \n",
" predicate | \n",
" object | \n",
" evidence_type | \n",
" publication | \n",
" source | \n",
"
\n",
" \n",
" \n",
" \n",
" uuid:70269c5a-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0011097 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" uuid:70269c5b-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0002187 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[('uuid:70269c5a-42a9-11ee-be37-31ef105c25ea', 'MONDO:0023659', 'biolink:has_phenotype', 'HP:0011097', 'ECO:0000269', 'PMID:31675180', 'infores:hpo-annotations'),\n",
" ('uuid:70269c5b-42a9-11ee-be37-31ef105c25ea', 'MONDO:0023659', 'biolink:has_phenotype', 'HP:0002187', 'ECO:0000269', 'PMID:31675180', 'infores:hpo-annotations')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT * FROM term_association LIMIT 2;"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e639def1-00e1-4113-9da6-fb2f32701952",
"metadata": {},
"outputs": [],
"source": [
"import sqlite3\n",
"import pandas as pd\n",
"from semsimian import Semsimian\n",
"from collections import Counter"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bb1e79e6-2b18-466f-95fb-d219f8c64431",
"metadata": {},
"outputs": [],
"source": [
"conn = sqlite3.connect(db)\n",
"res = conn.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
"# tables = res.fetchall()\n",
"\n",
"# tables"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e406c2fa-43e1-4f57-b8be-52334edfdbd6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" id | \n",
" subject | \n",
" predicate | \n",
" object | \n",
" evidence_type | \n",
" publication | \n",
" source | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" uuid:70269c5a-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0011097 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" 1 | \n",
" uuid:70269c5b-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0002187 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" 2 | \n",
" uuid:70269c5c-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0001518 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" 3 | \n",
" uuid:70269c5d-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0032792 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" 4 | \n",
" uuid:70269c5e-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0011451 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id subject \\\n",
"0 uuid:70269c5a-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"1 uuid:70269c5b-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"2 uuid:70269c5c-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"3 uuid:70269c5d-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"4 uuid:70269c5e-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"\n",
" predicate object evidence_type publication \\\n",
"0 biolink:has_phenotype HP:0011097 ECO:0000269 PMID:31675180 \n",
"1 biolink:has_phenotype HP:0002187 ECO:0000269 PMID:31675180 \n",
"2 biolink:has_phenotype HP:0001518 ECO:0000269 PMID:31675180 \n",
"3 biolink:has_phenotype HP:0032792 ECO:0000269 PMID:31675180 \n",
"4 biolink:has_phenotype HP:0011451 ECO:0000269 PMID:31675180 \n",
"\n",
" source \n",
"0 infores:hpo-annotations \n",
"1 infores:hpo-annotations \n",
"2 infores:hpo-annotations \n",
"3 infores:hpo-annotations \n",
"4 infores:hpo-annotations "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_term_association = pd.read_sql_query(\"SELECT * FROM term_association\", conn)\n",
"df_term_association.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "76a44b5a-2532-44a4-8226-e74322e289c9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['MONDO', 'HGNC', 'WB', 'MGI', 'RGD', 'Xenbase', 'ZFIN'],\n",
" dtype=object)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_term_association['subject'].str.split(\":\").str[0].unique()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "23cd279e-77fd-472c-bd13-d21753d3cf9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['HP', 'WBPhenotype', 'MP', 'XPO', 'ZP'], dtype=object)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_term_association['object'].str.split(\":\").str[0].unique()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f8af957d-612c-4922-998e-760553f171ad",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" subject | \n",
" predicate | \n",
" object | \n",
"
\n",
" \n",
" \n",
" \n",
" 217892 | \n",
" MONDO:0008260 | \n",
" biolink:has_phenotype | \n",
" HP:0010047 | \n",
"
\n",
" \n",
" 231074 | \n",
" MONDO:0035763 | \n",
" biolink:has_phenotype | \n",
" HP:0002907 | \n",
"
\n",
" \n",
" 149618 | \n",
" MONDO:0014823 | \n",
" biolink:has_phenotype | \n",
" HP:0002650 | \n",
"
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" \n",
" 217262 | \n",
" MONDO:0010674 | \n",
" biolink:has_phenotype | \n",
" HP:0000762 | \n",
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\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" subject predicate object\n",
"217892 MONDO:0008260 biolink:has_phenotype HP:0010047\n",
"231074 MONDO:0035763 biolink:has_phenotype HP:0002907\n",
"149618 MONDO:0014823 biolink:has_phenotype HP:0002650\n",
"217262 MONDO:0010674 biolink:has_phenotype HP:0000762"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_100 = df_term_association[df_term_association['subject'].str.startswith(\"MONDO:\")].sample(n=4, random_state=1)\n",
"df_100 = df_100[['subject', 'predicate', 'object']]\n",
"df_100"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "68078a59-7ac9-419a-af3b-9b4e089c3e4f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"217892 MONDO:0008260\n",
"231074 MONDO:0035763\n",
"149618 MONDO:0014823\n",
"217262 MONDO:0010674\n",
"Name: subject, dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_100['subject'].drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b0440238-5be2-4d0d-b017-ce6e0e82deaa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'MONDO:0008260': {'HP:0010047'},\n",
" 'MONDO:0035763': {'HP:0002907'},\n",
" 'MONDO:0014823': {'HP:0002650'},\n",
" 'MONDO:0010674': {'HP:0000762'}}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subject_object_dict = {}\n",
"for subject in df_100['subject'].drop_duplicates():\n",
" objects = set(df_100[df_100['subject']==subject]['object'])\n",
" subject_object_dict[subject] = objects\n",
"subject_object_dict"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "33d93d2c-a3cd-4a97-8c5c-51f8a874c24b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 15.4 s, sys: 1.2 s, total: 16.6 s\n",
"Wall time: 17.1 s\n"
]
}
],
"source": [
"%%time\n",
"predicates = [\"rdfs:subClassOf\", \"BFO:0000050\"]\n",
"semsimian = Semsimian(\n",
" spo=None,\n",
" predicates=predicates,\n",
" pairwise_similarity_attributes=None,\n",
" resource_path=db,\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "11fe4c5a-acde-4f4c-8228-e193ffa5792a",
"metadata": {},
"outputs": [],
"source": [
"tsps_dict = {}\n",
"\n",
"for subject_1 in df_100['subject'].drop_duplicates():\n",
" inner_dict = {}\n",
" for subject_2 in df_100['subject'].drop_duplicates():\n",
" inner_dict[subject_2] = semsimian.termset_pairwise_similarity(subject_object_dict[subject_1], subject_object_dict[subject_2])\n",
" tsps_dict[subject_1] = inner_dict\n",
" # tsps_dict[subject_1+\"_\"+subject_2] = semsimian.termset_pairwise_similarity(subject_object_dict[subject_1], subject_object_dict[subject_2])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "a8ab4ca7-9589-443f-a8d0-e65de6289e30",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'MONDO:0008260': {'MONDO:0008260': {'subject_termset': [{'HP:0010047': {'id': 'HP:0010047',\n",
" 'label': 'Short 5th metacarpal (HPO)'}}],\n",
" 'object_termset': [{'HP:0010047': {'id': 'HP:0010047',\n",
" 'label': 'Short 5th metacarpal (HPO)'}}],\n",
" 'subject_best_matches': {'HP:0010047': {'match_source': 'HP:0010047',\n",
" 'match_source_label': 'Short 5th metacarpal (HPO)',\n",
" 'match_target': 'HP:0010047',\n",
" 'match_target_label': 'Short 5th metacarpal (HPO)',\n",
" 'score': '18.008877205969117'},\n",
" 'similarity': {'HP:0010047': {'ancestor_id': 'HP:0010047',\n",
" 'ancestor_information_content': '18.008877205969117',\n",
" 'ancestor_label': 'Short 5th metacarpal (HPO)',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '1',\n",
" 'object_id': 'HP:0010047',\n",
" 'phenodigm_score': '4.243686746918193',\n",
" 'subject_id': 'HP:0010047'}}},\n",
" 'object_best_matches': {'HP:0010047': {'match_source': 'HP:0010047',\n",
" 'match_source_label': 'Short 5th metacarpal (HPO)',\n",
" 'match_target': 'HP:0010047',\n",
" 'match_target_label': 'Short 5th metacarpal (HPO)',\n",
" 'score': '18.008877205969117'},\n",
" 'similarity': {'HP:0010047': {'ancestor_id': 'HP:0010047',\n",
" 'ancestor_information_content': '18.008877205969117',\n",
" 'ancestor_label': 'Short 5th metacarpal (HPO)',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '1',\n",
" 'object_id': 'HP:0010047',\n",
" 'phenodigm_score': '4.243686746918193',\n",
" 'subject_id': 'HP:0010047'}}},\n",
" 'average_score': 18.008877205969117,\n",
" 'best_score': 18.008877205969117,\n",
" 'metric': 'ancestor_information_content'},\n",
" 'MONDO:0035763': {'subject_termset': [{'HP:0010047': {'id': 'HP:0010047',\n",
" 'label': 'Short 5th metacarpal (HPO)'}}],\n",
" 'object_termset': [{'HP:0002907': {'id': 'HP:0002907',\n",
" 'label': 'Microscopic hematuria (HPO)'}}],\n",
" 'subject_best_matches': {'HP:0010047': {'match_source': 'HP:0010047',\n",
" 'match_source_label': 'Short 5th metacarpal (HPO)',\n",
" 'match_target': 'HP:0002907',\n",
" 'match_target_label': 'Microscopic hematuria (HPO)',\n",
" 'score': '4.0111684575082664'},\n",
" 'similarity': {'HP:0010047': {'ancestor_id': 'HP:0000118',\n",
" 'ancestor_information_content': '4.0111684575082664',\n",
" 'ancestor_label': '',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '0.1188118811881188',\n",
" 'object_id': 'HP:0002907',\n",
" 'phenodigm_score': '0.690343733366938',\n",
" 'subject_id': 'HP:0010047'}}},\n",
" 'object_best_matches': {'HP:0002907': {'match_source': 'HP:0002907',\n",
" 'match_source_label': 'Microscopic hematuria (HPO)',\n",
" 'match_target': 'HP:0010047',\n",
" 'match_target_label': 'Short 5th metacarpal (HPO)',\n",
" 'score': '4.0111684575082664'},\n",
" 'similarity': {'HP:0002907': {'ancestor_id': 'HP:0000118',\n",
" 'ancestor_information_content': '4.0111684575082664',\n",
" 'ancestor_label': '',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '0.1188118811881188',\n",
" 'object_id': 'HP:0010047',\n",
" 'phenodigm_score': '0.690343733366938',\n",
" 'subject_id': 'HP:0002907'}}},\n",
" 'average_score': 4.0111684575082664,\n",
" 'best_score': 4.0111684575082664,\n",
" 'metric': 'ancestor_information_content'},\n",
" 'MONDO:0014823': {'subject_termset': [{'HP:0010047': {'id': 'HP:0010047',\n",
" 'label': 'Short 5th metacarpal (HPO)'}}],\n",
" 'object_termset': [{'HP:0002650': {'id': 'HP:0002650',\n",
" 'label': 'Scoliosis (HPO)'}}],\n",
" 'subject_best_matches': {'HP:0010047': {'match_source': 'HP:0010047',\n",
" 'match_source_label': 'Short 5th metacarpal (HPO)',\n",
" 'match_target': 'HP:0002650',\n",
" 'match_target_label': 'Scoliosis (HPO)',\n",
" 'score': '6.041650947133124'},\n",
" 'similarity': {'HP:0010047': {'ancestor_id': 'HP:0011842',\n",
" 'ancestor_information_content': '6.041650947133124',\n",
" 'ancestor_label': '',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '0.26582278481012656',\n",
" 'object_id': 'HP:0002650',\n",
" 'phenodigm_score': '1.267283898586921',\n",
" 'subject_id': 'HP:0010047'}}},\n",
" 'object_best_matches': {'HP:0002650': {'match_source': 'HP:0002650',\n",
" 'match_source_label': 'Scoliosis (HPO)',\n",
" 'match_target': 'HP:0010047',\n",
" 'match_target_label': 'Short 5th metacarpal (HPO)',\n",
" 'score': '6.041650947133124'},\n",
" 'similarity': {'HP:0002650': {'ancestor_id': 'HP:0011842',\n",
" 'ancestor_information_content': '6.041650947133124',\n",
" 'ancestor_label': '',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '0.26582278481012656',\n",
" 'object_id': 'HP:0010047',\n",
" 'phenodigm_score': '1.267283898586921',\n",
" 'subject_id': 'HP:0002650'}}},\n",
" 'average_score': 6.041650947133124,\n",
" 'best_score': 6.041650947133124,\n",
" 'metric': 'ancestor_information_content'},\n",
" 'MONDO:0010674': {'subject_termset': [{'HP:0010047': {'id': 'HP:0010047',\n",
" 'label': 'Short 5th metacarpal (HPO)'}}],\n",
" 'object_termset': [{'HP:0000762': {'id': 'HP:0000762',\n",
" 'label': 'Decreased nerve conduction velocity (HPO)'}}],\n",
" 'subject_best_matches': {'HP:0010047': {'match_source': 'HP:0010047',\n",
" 'match_source_label': 'Short 5th metacarpal (HPO)',\n",
" 'match_target': 'HP:0000762',\n",
" 'match_target_label': 'Decreased nerve conduction velocity (HPO)',\n",
" 'score': '4.0111684575082664'},\n",
" 'similarity': {'HP:0010047': {'ancestor_id': 'HP:0000118',\n",
" 'ancestor_information_content': '4.0111684575082664',\n",
" 'ancestor_label': '',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '0.14736842105263157',\n",
" 'object_id': 'HP:0000762',\n",
" 'phenodigm_score': '0.7688430022827241',\n",
" 'subject_id': 'HP:0010047'}}},\n",
" 'object_best_matches': {'HP:0000762': {'match_source': 'HP:0000762',\n",
" 'match_source_label': 'Decreased nerve conduction velocity (HPO)',\n",
" 'match_target': 'HP:0010047',\n",
" 'match_target_label': 'Short 5th metacarpal (HPO)',\n",
" 'score': '4.0111684575082664'},\n",
" 'similarity': {'HP:0000762': {'ancestor_id': 'HP:0000118',\n",
" 'ancestor_information_content': '4.0111684575082664',\n",
" 'ancestor_label': '',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '0.14736842105263157',\n",
" 'object_id': 'HP:0010047',\n",
" 'phenodigm_score': '0.7688430022827241',\n",
" 'subject_id': 'HP:0000762'}}},\n",
" 'average_score': 4.0111684575082664,\n",
" 'best_score': 4.0111684575082664,\n",
" 'metric': 'ancestor_information_content'}},\n",
" 'MONDO:0035763': {'MONDO:0008260': {'subject_termset': [{'HP:0002907': {'id': 'HP:0002907',\n",
" 'label': 'Microscopic hematuria (HPO)'}}],\n",
" 'object_termset': [{'HP:0010047': {'id': 'HP:0010047',\n",
" 'label': 'Short 5th metacarpal (HPO)'}}],\n",
" 'subject_best_matches': {'HP:0002907': {'match_source': 'HP:0002907',\n",
" 'match_source_label': 'Microscopic hematuria (HPO)',\n",
" 'match_target': 'HP:0010047',\n",
" 'match_target_label': 'Short 5th metacarpal (HPO)',\n",
" 'score': '4.0111684575082664'},\n",
" 'similarity': {'HP:0002907': {'ancestor_id': 'HP:0000118',\n",
" 'ancestor_information_content': '4.0111684575082664',\n",
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" 'similarity': {'HP:0000762': {'ancestor_id': 'HP:0000762',\n",
" 'ancestor_information_content': '16.008877205969117',\n",
" 'ancestor_label': 'Decreased nerve conduction velocity (HPO)',\n",
" 'cosine_similarity': 'NaN',\n",
" 'jaccard_similarity': '1',\n",
" 'object_id': 'HP:0000762',\n",
" 'phenodigm_score': '4.001109496873226',\n",
" 'subject_id': 'HP:0000762'}}},\n",
" 'average_score': 16.008877205969117,\n",
" 'best_score': 16.008877205969117,\n",
" 'metric': 'ancestor_information_content'}}}"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tsps_dict"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f5ffa45a-0223-426b-966b-3ddcebd5b180",
"metadata": {},
"outputs": [],
"source": [
"\n",
"subject_prefixes = [\"MGI:\"]\n",
"# object_terms = set(df_100['object'].drop_duplicates())\n",
"assoc_predicate = {\"biolink:has_phenotype\"}\n",
"include_similarity_object = True\n",
"limit = 50\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e17e45a8-a80b-451c-a8a8-064bcfdb979e",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"CPU times: user 1min 55s, sys: 2.48 s, total: 1min 57s\n",
"Wall time: 1min 36s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"search_type = \"flat\"\n",
"flat_result = {}\n",
"for subj, obj in subject_object_dict.items():\n",
" flat_result[subj] = semsimian.associations_search(\n",
" assoc_predicate,\n",
" set(obj),\n",
" include_similarity_object,\n",
" search_type,\n",
" None,\n",
" subject_prefixes,\n",
" limit,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "28cc65ae-5384-4930-abd5-c8ca6a2ac425",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(flat_result)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "64c75fc3-e80c-4e2e-8f6a-dfc73067cd86",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"CPU times: user 2h 20min 25s, sys: 4min 40s, total: 2h 25min 5s\n",
"Wall time: 2h 24min 47s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"search_type = \"full\"\n",
"full_result = {}\n",
"for subj, obj in subject_object_dict.items():\n",
" full_result[subj] = semsimian.associations_search(\n",
" assoc_predicate,\n",
" set(obj),\n",
" include_similarity_object,\n",
" search_type,\n",
" None,\n",
" subject_prefixes,\n",
" limit,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "d7893cfb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(full_result)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "2fa0aa3c-1c15-42b0-b183-d0696bb3539c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"CPU times: user 21min 44s, sys: 20.5 s, total: 22min 4s\n",
"Wall time: 21min 40s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"search_type = \"hybrid\"\n",
"hybrid_result = {}\n",
"for subj, obj in subject_object_dict.items():\n",
" hybrid_result[subj] = semsimian.associations_search(\n",
" assoc_predicate,\n",
" set(obj),\n",
" include_similarity_object,\n",
" search_type,\n",
" None,\n",
" subject_prefixes,\n",
" limit,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "af58c33b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(hybrid_result)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "ce99423f-6e90-440d-8d7f-eb4689d6f9ed",
"metadata": {},
"outputs": [],
"source": [
"def get_search_terms_for_input_term(dictionary:dict):\n",
" result_dict = {}\n",
" for k, v in dictionary.items():\n",
" result_dict[k] = [curie for _,_,curie in v]\n",
" return result_dict\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "154aedab-3fc5-4f15-aade-74efbbd3c826",
"metadata": {},
"outputs": [],
"source": [
"full_result_terms = get_search_terms_for_input_term(full_result)\n",
"flat_result_terms = get_search_terms_for_input_term(flat_result)\n",
"hybrid_result_terms = get_search_terms_for_input_term(hybrid_result)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "bcbb7a29-a37c-4f29-b93b-620a84fa6f2c",
"metadata": {},
"outputs": [],
"source": [
"def get_overlap(dict1, dict2):\n",
" input_term_overlap = {}\n",
" for term, result in dict1.items():\n",
" common = set(result).intersection(set(dict2[term]))\n",
" total = len(set(dict2[term]) | set(result))\n",
" overlap = len(common) / total * 100\n",
" input_term_overlap[term] = overlap\n",
" return input_term_overlap\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "d1a6a3cf-935c-48d8-b585-d7e14845c821",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'MGI:1261425': 14.942528735632186,\n",
" 'MGI:1858212': 31.57894736842105,\n",
" 'MGI:109207': 61.29032258064516,\n",
" 'MGI:98475': 11.11111111111111,\n",
" 'MGI:97512': 25.0,\n",
" 'MGI:88291': 33.33333333333333,\n",
" 'MGI:1914362': 49.25373134328358,\n",
" 'MGI:1924285': 7.526881720430108,\n",
" 'MGI:2385958': 8.695652173913043,\n",
" 'MGI:1313275': 17.647058823529413,\n",
" 'MGI:2177178': 19.047619047619047,\n",
" 'MGI:98973': 13.636363636363635,\n",
" 'MGI:1341163': 4.166666666666666,\n",
" 'MGI:97311': 35.13513513513514,\n",
" 'MGI:88258': 53.84615384615385,\n",
" 'MGI:1915325': 26.582278481012654,\n",
" 'MGI:96083': 25.0,\n",
" 'MGI:95808': 51.515151515151516,\n",
" 'MGI:104779': 9.89010989010989,\n",
" 'MGI:104673': 29.87012987012987,\n",
" 'MGI:1321392': 75.43859649122807,\n",
" 'MGI:97348': 56.25,\n",
" 'MGI:1339999': 85.18518518518519,\n",
" 'MGI:88145': 35.13513513513514,\n",
" 'MGI:1098280': 44.927536231884055,\n",
" 'MGI:1891740': 2.0408163265306123,\n",
" 'MGI:1346872': 14.942528735632186,\n",
" 'MGI:105098': 35.13513513513514,\n",
" 'MGI:97503': 38.88888888888889,\n",
" 'MGI:2673128': 36.986301369863014,\n",
" 'MGI:1196326': 28.205128205128204,\n",
" 'MGI:102780': 2.0408163265306123,\n",
" 'MGI:95664': 21.951219512195124,\n",
" 'MGI:2385599': 21.951219512195124,\n",
" 'MGI:894293': 21.951219512195124,\n",
" 'MGI:1918686': 28.205128205128204,\n",
" 'MGI:97740': 19.047619047619047,\n",
" 'MGI:1859183': 4.166666666666666,\n",
" 'MGI:2145823': 35.13513513513514,\n",
" 'MGI:1920999': 12.359550561797752,\n",
" 'MGI:107543': 23.456790123456788,\n",
" 'MGI:1347470': 13.636363636363635,\n",
" 'MGI:1261768': 28.205128205128204,\n",
" 'MGI:1914523': 63.934426229508205,\n",
" 'MGI:1335098': 66.66666666666666,\n",
" 'MGI:892003': 35.13513513513514,\n",
" 'MGI:88342': 53.84615384615385,\n",
" 'MGI:108417': 44.927536231884055,\n",
" 'MGI:1195272': 26.582278481012654,\n",
" 'MGI:2137356': 1.0101010101010102,\n",
" 'MGI:107364': 11.11111111111111,\n",
" 'MGI:2135607': 17.647058823529413,\n",
" 'MGI:1346013': 26.582278481012654,\n",
" 'MGI:96958': 12.359550561797752,\n",
" 'MGI:96606': 5.263157894736842,\n",
" 'MGI:96721': 7.526881720430108,\n",
" 'MGI:1194993': 88.67924528301887,\n",
" 'MGI:2684139': 35.13513513513514,\n",
" 'MGI:1339753': 88.67924528301887,\n",
" 'MGI:1916296': 7.526881720430108,\n",
" 'MGI:97525': 25.0,\n",
" 'MGI:96551': 11.11111111111111,\n",
" 'MGI:1098590': 42.857142857142854,\n",
" 'MGI:1891124': 21.951219512195124,\n",
" 'MGI:1096335': 21.951219512195124,\n",
" 'MGI:87986': 20.481927710843372,\n",
" 'MGI:1276116': 17.647058823529413,\n",
" 'MGI:1098767': 17.647058823529413,\n",
" 'MGI:1321395': 17.647058823529413,\n",
" 'MGI:1261423': 21.951219512195124,\n",
" 'MGI:1917579': 11.11111111111111,\n",
" 'MGI:1929601': 21.951219512195124,\n",
" 'MGI:1196389': 96.07843137254902,\n",
" 'MGI:101884': 49.25373134328358,\n",
" 'MGI:98923': 25.0,\n",
" 'MGI:2444248': 16.27906976744186,\n",
" 'MGI:95514': 25.0,\n",
" 'MGI:1346060': 23.456790123456788,\n",
" 'MGI:107537': 19.047619047619047,\n",
" 'MGI:1914701': 16.27906976744186,\n",
" 'MGI:108520': 38.88888888888889,\n",
" 'MGI:1924238': 96.07843137254902,\n",
" 'MGI:1914155': 47.05882352941176,\n",
" 'MGI:88084': 16.27906976744186,\n",
" 'MGI:2684944': 36.986301369863014,\n",
" 'MGI:2141861': 40.845070422535215,\n",
" 'MGI:1919202': 14.942528735632186,\n",
" 'MGI:1353592': 2.0408163265306123,\n",
" 'MGI:1206581': 13.636363636363635,\n",
" 'MGI:1277238': 49.25373134328358,\n",
" 'MGI:3045253': 17.647058823529413,\n",
" 'MGI:103560': 58.730158730158735,\n",
" 'MGI:2387643': 29.87012987012987,\n",
" 'MGI:95481': 25.0,\n",
" 'MGI:104644': 5.263157894736842,\n",
" 'MGI:88417': 16.27906976744186,\n",
" 'MGI:104510': 21.951219512195124,\n",
" 'MGI:103289': 81.81818181818183,\n",
" 'MGI:98834': 16.27906976744186,\n",
" 'MGI:1920719': 11.11111111111111}"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# input_term_full_flat_overlap = get_overlap(full_result_terms, flat_result_terms)\n",
"input_term_full_hybrid_overlap = get_overlap(full_result_terms, hybrid_result_terms)\n",
"input_term_full_flat_overlap = get_overlap(full_result_terms, flat_result_terms)\n",
"input_term_flat_hybrid_overlap = get_overlap(flat_result_terms, hybrid_result_terms)\n",
"\n",
"input_term_flat_hybrid_overlap\n",
"# print(len(set(full_result_terms['MGI:1261425']).intersection(set(full_result_terms['MGI:1261425']))))"
]
},
{
"cell_type": "markdown",
"id": "c5c76fc6-4ed9-4c30-8085-9da558f50031",
"metadata": {},
"source": [
"### Plot params"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "ebc0af67-8b45-45bf-9196-9a878c10766d",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"\n",
"def plot_graph(data, title):\n",
" # Create lists for the plot\n",
" keys = list(data.keys())\n",
" values = list(data.values())\n",
" \n",
" # Create a bar chart\n",
" plt.figure(figsize=(20,10)) # Increase the size as needed\n",
" bars = plt.bar(range(len(data)), values, tick_label=keys)\n",
" \n",
" # Rotate the x-axis labels so they don't overlap\n",
" plt.xticks(rotation=90)\n",
" \n",
" # Add title and labels\n",
" plt.title(title)\n",
" plt.xlabel('Keys')\n",
" plt.ylabel('Values')\n",
"\n",
" # Loop over the bars and add the value on top\n",
" for bar in bars:\n",
" yval = bar.get_height()\n",
" plt.text(bar.get_x() + bar.get_width()/2.0, yval, round(yval, 2), va='bottom', rotation=45) # va: vertical alignment\n",
" \n",
" # Show the plot\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "8ea8aa39-02c1-48e8-a319-cee7723733d7",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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