{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01386753,0.001359062,0.002319764,0.001302085,0.0005770164,0.003227414,0.00288669,0.001675043,0.00134242],"category_scores_gemma":[0.0480344,0.001014405,0.001744135,0.001331226,0.001766316,0.003816375,0.001822896,0.002924622,0.0005952337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553925,"about_ca_system_score_gemma":0.002465359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004983471,"about_ca_topic_score_gemma":0.004499287,"domain_scores_codex":[0.9942373,0.003447396,0.0003864307,0.0008905568,0.0008858332,0.0001525425],"domain_scores_gemma":[0.9664338,0.02588722,0.001353958,0.005091738,0.0009940263,0.0002392535],"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":[0.0001177358,0.0001022611,0.005633736,0.0001076846,0.000117171,0.00008518601,0.00005393071,0.9581413,0.003571781,0.009927695,0.0004277639,0.02171371],"study_design_scores_gemma":[0.00001068897,0.00001494658,0.0004273452,0.000006760719,0.00001261601,0.0000109992,0.000004597997,0.9885821,0.0009184451,0.00977995,0.0002211152,0.00001044827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04469482,0.0002571962,0.9511814,0.001039371,0.00003804596,0.00007922973,0.0003305547,0.001435472,0.0009438614],"genre_scores_gemma":[0.5543103,0.0004060782,0.4425939,0.0004351086,0.00006626364,0.0003821506,0.0009365883,0.0004582648,0.000411395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01386753,"threshold_uncertainty_score":0.07333934,"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."}}