{"id":"W4293770727","doi":"10.3389/fbinf.2022.960889","title":"Sparse bayesian learning for genomic selection in yeast","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Machine learning; Bayesian probability; Artificial intelligence; Trait; Ranking (information retrieval); Selection (genetic algorithm); Computer science; Relevance (law); Heritability; Biology; Evolutionary biology","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":[0.003766195,0.0007322807,0.00160626,0.0009832962,0.0004108027,0.001024053,0.001408297,0.001075738,0.001098619],"category_scores_gemma":[0.01271968,0.0004879599,0.0008379295,0.001145849,0.0008124545,0.001357697,0.001200474,0.001662547,0.0003916314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035224,"about_ca_system_score_gemma":0.001305338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01019932,"about_ca_topic_score_gemma":0.008967472,"domain_scores_codex":[0.998893,0.0005961673,0.00005089269,0.0001776785,0.0001990254,0.00008337262],"domain_scores_gemma":[0.9937983,0.004809692,0.0003030785,0.0002555723,0.000675076,0.000158273],"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":[0.0001606594,0.00005727494,0.002412638,0.00006270835,0.00007979452,0.00004309426,0.00007912742,0.8984512,0.000966239,0.009350681,0.001143684,0.08719291],"study_design_scores_gemma":[0.000009128426,0.000008800482,0.0001455584,0.00000386558,0.000004072126,0.000004628303,0.000004711628,0.9923856,0.0001615077,0.007148798,0.0001197448,0.000003588111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03745589,0.0007129743,0.9600774,0.0004336864,0.0000263873,0.00003281804,0.0002018279,0.0005645332,0.0004945123],"genre_scores_gemma":[0.6712186,0.001100008,0.3224462,0.0004190141,0.0001522467,0.0002496423,0.001761189,0.0001545173,0.002498594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01019932,"threshold_uncertainty_score":0.02027988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008078267660177099,"score_gpt":0.2027325042530254,"score_spread":0.1946542365928483,"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."}}