{"id":"W2914171828","doi":"10.1093/database/bay147","title":"Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine","year":2018,"lang":"en","type":"article","venue":"Database","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health","keywords":"Computer science; Task (project management); Precision medicine; Triage; F1 score; Relationship extraction; Precision and recall; Annotation; Information extraction; Named-entity recognition; Information retrieval; Relation (database); Personalized medicine; Natural language processing; Data science; Data mining; Artificial intelligence; Bioinformatics; Medicine; Genetics; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.007600836,0.00228296,0.00162076,0.01190554,0.001497688,0.005045407,0.003246469,0.002127672,0.0151253],"category_scores_gemma":[0.01031174,0.001263972,0.002426191,0.008776105,0.0004050641,0.004534935,0.002454452,0.002001177,0.01654954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001982144,"about_ca_system_score_gemma":0.004743723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0167432,"about_ca_topic_score_gemma":0.02246185,"domain_scores_codex":[0.9946636,0.0007137471,0.0006103887,0.001475048,0.002169426,0.0003677212],"domain_scores_gemma":[0.9913424,0.002662133,0.000747712,0.001419195,0.002947063,0.0008814612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001011157,0.0006716491,0.01560977,0.003946405,0.0006113498,0.0003350508,0.0004932967,0.006309255,0.01987312,0.00404181,0.5350568,0.4120403],"study_design_scores_gemma":[0.0005047187,0.001256535,0.02747666,0.0009612133,0.0005565912,0.001174118,0.0002564573,0.06117418,0.02523176,0.006237088,0.874907,0.000263693],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.03557273,0.03056976,0.3055376,0.006009399,0.001365529,0.004853991,0.4222121,0.1481242,0.0457548],"genre_scores_gemma":[0.02288742,0.005556324,0.3137175,0.001317606,0.0004376132,0.002119886,0.6337751,0.003149193,0.0170394],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0167432,"threshold_uncertainty_score":0.05059916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07326093043933397,"score_gpt":0.3871233921750868,"score_spread":0.3138624617357528,"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."}}