{"id":"W2887711510","doi":"10.1038/s41598-019-40561-2","title":"Interpretable genotype-to-phenotype classifiers with performance guarantees","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canada Research Chairs; Government of Canada; Compute Canada; Université Laval","keywords":"Interpretability; Machine learning; Computer science; Artificial intelligence; Scalability; Curse of dimensionality; Generalization; Turnkey; Mathematics","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":[0.0005487816,0.0001473799,0.0001343646,0.00008374186,0.0001348653,0.0001430291,0.0002045752,0.00007564116,0.0001324596],"category_scores_gemma":[0.00006755553,0.0001145278,0.00004371725,0.000234727,0.0001059654,0.00001346141,0.0001336893,0.0001118556,0.00023121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001748088,"about_ca_system_score_gemma":0.0001385637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006799797,"about_ca_topic_score_gemma":0.00001013005,"domain_scores_codex":[0.9986022,0.00001700141,0.0002772193,0.0004943582,0.0003011867,0.0003080017],"domain_scores_gemma":[0.9986231,0.000004447346,0.0001618245,0.0009774837,0.000136217,0.00009692604],"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":[0.0003141006,0.0001092753,0.2799732,0.0002212721,0.0001252105,0.00002846954,0.001155613,0.01535422,0.6484696,0.0001324987,0.04728903,0.006827521],"study_design_scores_gemma":[0.0003856497,0.0008264869,0.0163319,0.0001329842,0.00002910395,0.0003701155,0.0002052842,0.006406235,0.1235116,0.0001025429,0.850999,0.0006990708],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546636,0.00004777213,0.001250222,0.00004131725,0.00212185,0.0002917131,0.000001071635,0.00002899586,0.04155343],"genre_scores_gemma":[0.9728314,0.000002729393,0.003614056,0.0001630575,0.00004848031,0.00001019115,0.00007450837,0.00001980135,0.02323573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.80371,"threshold_uncertainty_score":0.4670309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003900188324957552,"score_gpt":0.2139763805367878,"score_spread":0.2100761922118303,"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."}}