{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003656371,0.0001032391,0.0001576,0.00004566834,0.0001360428,0.000003853531,0.0001541912,0.0000639827,0.00006449738],"category_scores_gemma":[0.002363009,0.00005861583,0.00003309932,0.0001321446,0.0007471367,0.000006263047,0.0001301984,0.00005714258,0.000001126951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005825013,"about_ca_system_score_gemma":0.00003371853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005818109,"about_ca_topic_score_gemma":0.0001100693,"domain_scores_codex":[0.9991817,0.00007478458,0.0002476328,0.0002522478,0.0001287034,0.0001149197],"domain_scores_gemma":[0.9991452,0.0001801872,0.0001377969,0.0003345426,0.0001473141,0.00005502225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002943175,0.00008085088,0.0003578714,0.0001059335,0.00006258376,5.833111e-7,0.0009361195,3.228634e-7,0.8125358,0.0002522801,0.02877603,0.1565973],"study_design_scores_gemma":[0.003073554,0.003237747,0.008418423,0.002766217,0.0002039296,0.00004004377,0.002552429,0.0005294118,0.4916208,0.001054236,0.4862135,0.0002897705],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582034,0.003882903,0.03115817,0.004018608,0.0004341729,0.0008917603,0.0006820555,0.000019626,0.0007093024],"genre_scores_gemma":[0.9872432,0.000271616,0.0102333,0.0002433686,0.0004900059,0.00008642489,0.0006876171,0.00001397107,0.0007305174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4574375,"threshold_uncertainty_score":0.2828913,"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."}}