{"id":"W2951055794","doi":"10.1101/388348","title":"Interpretable genotype-to-phenotype classifiers with performance guarantees","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Compute Canada; Université Laval","keywords":"Interpretability; Machine learning; Artificial intelligence; Computer science; Scalability; Curse of dimensionality; Generalization; Turnkey; Limit (mathematics); Interpretation (philosophy); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000477372,0.0006628749,0.0004800031,0.0001962639,0.0002017303,0.0002001698,0.0009633293,0.0006364968,0.00007372103],"category_scores_gemma":[0.0002065595,0.0006151155,0.0001150609,0.0003009048,0.0002238334,0.00001626622,0.0009507806,0.0007068249,0.000223264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001072815,"about_ca_system_score_gemma":0.0005896246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002082933,"about_ca_topic_score_gemma":0.000005807159,"domain_scores_codex":[0.9974067,0.00008103171,0.0005025497,0.0009304741,0.0003818941,0.0006973956],"domain_scores_gemma":[0.996932,0.00001419318,0.0003888335,0.001809251,0.0005657313,0.0002899967],"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":[0.0008285547,0.0001560644,0.046096,0.001107199,0.0007036082,0.00001616682,0.00007105569,0.002798938,0.9405208,0.000115119,0.00756532,0.00002122153],"study_design_scores_gemma":[0.001490282,0.002276027,0.128046,0.001459954,0.0003335059,2.595916e-7,0.00001313335,0.01017106,0.6957721,0.000001351875,0.1569864,0.003449951],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852409,0.0003192478,0.01221843,0.0001068833,0.0008574967,0.0006732956,0.0000908119,0.0001770516,0.0003159038],"genre_scores_gemma":[0.9696916,0.0001414876,0.02842947,0.0006748328,0.0007049238,0.0001161996,0.000002906888,0.0001656822,0.00007288274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2447487,"threshold_uncertainty_score":0.99963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006912013707576995,"score_gpt":0.212896092900878,"score_spread":0.205984079193301,"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."}}