{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00104246,0.0009442519,0.0006274075,0.0003973627,0.0002529593,0.0007790613,0.001076853,0.001042405,0.002070058],"category_scores_gemma":[0.004333785,0.0003630106,0.0003571097,0.0006319438,0.0005340916,0.001702234,0.0006997435,0.002045793,0.000719557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006728083,"about_ca_system_score_gemma":0.0005253299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005610518,"about_ca_topic_score_gemma":0.006961867,"domain_scores_codex":[0.9996707,0.00008502675,0.00001561139,0.0001240589,0.00006222203,0.00004230462],"domain_scores_gemma":[0.9983999,0.001063158,0.0001427772,0.0002137042,0.0001392267,0.00004117166],"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":[0.000302742,0.0001202309,0.01179747,0.0001760199,0.0002156861,0.0001372871,0.00007364064,0.6843252,0.01084072,0.01334469,0.00322046,0.2754458],"study_design_scores_gemma":[0.000008607961,0.00002542838,0.001424303,0.00001283178,0.00001779208,0.00002712096,0.000009061263,0.9830645,0.00245312,0.01229645,0.0006533714,0.0000074443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1452382,0.001442884,0.8453494,0.0009413444,0.0001041986,0.00003282656,0.0009109269,0.002035869,0.003944513],"genre_scores_gemma":[0.8952129,0.0005646599,0.0983689,0.0003938938,0.00004047434,0.00006146866,0.001324394,0.0001015554,0.003931641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005610518,"threshold_uncertainty_score":0.01115572,"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."}}