{"id":"W4307091043","doi":"10.1093/bioinformatics/btac692","title":"Defining the extent of gene function using ROC curvature","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. National Library of Medicine; National Institute of Mental Health; National Institutes of Health","keywords":"Genome; Biology; Computational biology; Robustness (evolution); Gene; Generalizability theory; Context (archaeology); Computer science; Function (biology); Genetics; Mathematics","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":[0.0003463721,0.000112142,0.0001092551,0.00003393518,0.0002965081,0.0000199351,0.0002375502,0.00006792483,0.00003206137],"category_scores_gemma":[0.00001495908,0.00008593036,0.00009413272,0.000131417,0.00004013691,0.000006477953,0.0002528789,0.0001557167,0.000004157618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001839271,"about_ca_system_score_gemma":0.00008279562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005503291,"about_ca_topic_score_gemma":0.000002165239,"domain_scores_codex":[0.9991453,0.00002600968,0.000375528,0.00007543873,0.000190631,0.0001870513],"domain_scores_gemma":[0.999293,0.00001018351,0.0002730151,0.0003399692,0.00004903104,0.00003476685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001456725,0.0006023421,0.00560169,0.0008833086,0.002106383,0.000005929415,0.01040895,0.4486527,0.1665785,0.05865708,0.1159013,0.189145],"study_design_scores_gemma":[0.002069657,0.0015526,0.00168889,0.00004196227,0.0003956832,0.0003280772,0.008809463,0.5058036,0.02324433,0.001303278,0.4537264,0.001036009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5633375,0.01751385,0.4016948,0.0003460296,0.003268277,0.001389649,0.0004860268,0.00006476337,0.0118991],"genre_scores_gemma":[0.9857895,0.00006145536,0.01254725,0.0009626008,0.0001388029,0.00001762743,0.0002946225,0.00001814818,0.0001699684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.422452,"threshold_uncertainty_score":0.3504138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009905996297909917,"score_gpt":0.2194426943435673,"score_spread":0.2095366980456574,"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."}}