{"id":"W3202451896","doi":"10.1002/tpg2.20147","title":"Deep neural networks for genomic prediction do not estimate marker effects","year":2021,"lang":"en","type":"article","venue":"The Plant Genome","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Global Institute for Water Security; University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Epistasis; Biology; Artificial neural network; Machine learning; Artificial intelligence; Predictive modelling; Computational biology; Computer science; Genetics; Gene","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":[0.0001342966,0.00014693,0.000121779,0.00001127966,0.0001506994,0.00003260709,0.0001918345,0.000110475,0.00001974225],"category_scores_gemma":[0.00002986641,0.0001141743,0.00008575966,0.00003747082,0.0000493773,0.000001641278,0.0001021318,0.00008762042,0.000007031036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009060481,"about_ca_system_score_gemma":0.00002774197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001936775,"about_ca_topic_score_gemma":0.000007958735,"domain_scores_codex":[0.9991388,0.00006702536,0.0001511431,0.0002913972,0.00006887964,0.0002827263],"domain_scores_gemma":[0.9994527,0.00006205597,0.00005163202,0.0003414074,0.00003739078,0.00005489182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000907747,0.0001167403,0.002233212,0.0001338954,0.0004081948,0.00000955818,0.0003135258,0.3599206,0.6225646,0.001785834,0.00233722,0.009268923],"study_design_scores_gemma":[0.003209121,0.001405951,0.7980075,0.00003472123,0.0005037194,0.0006474749,0.0001062506,0.119871,0.0349527,0.002557603,0.0377016,0.001002377],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7840353,0.00356566,0.2101648,0.0001845536,0.000861857,0.0005066826,0.0002123308,0.00002383464,0.0004450309],"genre_scores_gemma":[0.9919497,0.00005812381,0.005471251,0.0005377245,0.0007242289,0.00007210145,0.0006863482,0.00002718167,0.0004732891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7957743,"threshold_uncertainty_score":0.4655895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008523430110009764,"score_gpt":0.2125618240524697,"score_spread":0.20403839394246,"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."}}