{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001566269,0.0001993929,0.0003474193,0.0001563777,0.000499268,0.0001627168,0.0004236114,0.0002817498,0.000002960091],"category_scores_gemma":[0.001019473,0.0001752713,0.0001039782,0.0002697645,0.0004527912,0.00001453151,0.0004709951,0.0002525029,0.000003571368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001203441,"about_ca_system_score_gemma":0.0003196507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002230911,"about_ca_topic_score_gemma":0.00002633003,"domain_scores_codex":[0.9974843,0.0002632448,0.0003255928,0.000602197,0.0005560994,0.0007685197],"domain_scores_gemma":[0.9984604,0.0001961832,0.00008410494,0.0004335946,0.0003208015,0.0005048939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002822745,0.0002350453,0.1065665,0.0003740614,0.0006751122,0.00001372549,0.002423612,0.000004350291,0.7591395,0.001229966,0.008454219,0.1206017],"study_design_scores_gemma":[0.01474165,0.006377008,0.3241162,0.0004736076,0.0006976235,0.00003345038,0.07938778,0.003217072,0.189736,0.001020489,0.3770527,0.003146429],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919701,0.004371451,0.001086971,0.000505829,0.00008355418,0.0005503126,0.0008784741,0.00007637964,0.0004768929],"genre_scores_gemma":[0.995286,0.00007405983,0.002423526,0.00002787994,0.000659387,0.0001345192,0.0006510888,0.00004986202,0.0006936619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5694035,"threshold_uncertainty_score":0.7147358,"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."}}