{"id":"W1986657538","doi":"10.1093/nar/gkv383","title":"PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more","year":2015,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Genome Alberta; Canadian Institutes of Health Research; Alberta Innovates; Genome Canada","keywords":"DrugBank; UniProt; Information retrieval; Unified Medical Language System; Computer science; Computational biology; Ontology; Bioinformatics; Biology; Gene; Genetics; Drug","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002334898,0.002955641,0.001343046,0.009416708,0.0009971154,0.002025002,0.001622897,0.00114581,0.03297567],"category_scores_gemma":[0.006163288,0.0007325303,0.001886439,0.005421435,0.0004570015,0.00409243,0.002501973,0.0009357985,0.01903052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009357958,"about_ca_system_score_gemma":0.002881792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006060385,"about_ca_topic_score_gemma":0.01321118,"domain_scores_codex":[0.9983782,0.0002540484,0.0003224934,0.0004951094,0.0004704399,0.00007968034],"domain_scores_gemma":[0.9973438,0.001396601,0.0002541989,0.0002680197,0.0005484223,0.0001889235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001728724,0.0005111634,0.01332623,0.0070173,0.0006696909,0.002463188,0.001421468,0.00326708,0.03505164,0.01025598,0.4674744,0.4568131],"study_design_scores_gemma":[0.0006509479,0.0004329033,0.02093005,0.0007676319,0.0006115148,0.003389378,0.0008672359,0.08659542,0.04748129,0.01730772,0.8206486,0.0003173638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.02601327,0.002483858,0.2303273,0.001605818,0.0003986018,0.001938974,0.4218048,0.2918515,0.0235758],"genre_scores_gemma":[0.03152353,0.001489831,0.4271554,0.000787975,0.000162317,0.001125593,0.5083677,0.007923426,0.0214641],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03297567,"threshold_uncertainty_score":0.1103146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07102885753606152,"score_gpt":0.373355959485395,"score_spread":0.3023271019493335,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}