{"id":"W2590688707","doi":"10.1002/humu.23193","title":"Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling","year":2017,"lang":"en","type":"article","venue":"Human Mutation","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"National Institute of General Medical Sciences; Defense Advanced Research Projects Agency; National Institutes of Health; Cancer Prevention and Research Institute of Texas; National Science Foundation","keywords":"Biology; Phenotype; Genotype; Genotype-phenotype distinction; Computational biology; Genetics; Bioinformatics; Gene","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":[0.00008094817,0.0001095222,0.0000785242,0.00001493939,0.0006520555,0.0001648139,0.0001359457,0.00007818146,0.00001766517],"category_scores_gemma":[0.0001530624,0.0001161596,0.00002487639,0.000008896181,0.00006065199,0.000038877,0.000144088,0.00009251243,0.00000393259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001480263,"about_ca_system_score_gemma":0.00002322746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003297569,"about_ca_topic_score_gemma":0.00001985586,"domain_scores_codex":[0.99936,0.00001879408,0.0001844588,0.0001997407,0.0001209566,0.0001161054],"domain_scores_gemma":[0.9994672,0.00001297199,0.0002097841,0.000221717,0.00005760865,0.00003069682],"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":[0.00009502491,0.00007591528,0.02008257,0.0000812131,0.0001123725,0.000007209521,0.008306472,0.639689,0.323245,0.0009099185,0.0001132232,0.00728214],"study_design_scores_gemma":[0.0006660002,0.00008119163,0.01273493,0.00001858017,0.0000178867,0.000007193884,0.0006088543,0.9789302,0.006128361,0.0005956235,0.00003823338,0.0001729449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8998411,0.0001711868,0.0990411,0.00003998693,0.00007154707,0.0001092996,0.00001086365,0.00002026746,0.0006946127],"genre_scores_gemma":[0.9743026,0.000002700482,0.02469171,0.00009052107,0.0002544007,0.000007207716,0.0006028644,0.00001777661,0.00003017261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3392412,"threshold_uncertainty_score":0.501515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02634604656080341,"score_gpt":0.3129055952071076,"score_spread":0.2865595486463042,"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."}}