{"id":"W3159144092","doi":"10.1093/bib/bbab132","title":"KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beef Cattle Research Council; National Natural Science Foundation of China","keywords":"Cosine similarity; Best linear unbiased prediction; Similarity (geometry); Support vector machine; Heritability; Kernel (algebra); Computer science; Machine learning; Artificial intelligence; Mathematics; Biology; Pattern recognition (psychology); Genetics; Selection (genetic algorithm); Combinatorics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002563179,0.00020768,0.0001764262,0.00003945192,0.0001956706,0.00006833684,0.0002567121,0.0001628697,0.00001205583],"category_scores_gemma":[0.0001013824,0.0001581729,0.00006796047,0.0001698337,0.0001261563,0.000008650134,0.0001902846,0.0003216283,0.000006999543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003002446,"about_ca_system_score_gemma":0.000231401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007120838,"about_ca_topic_score_gemma":0.00008453363,"domain_scores_codex":[0.998807,0.00005311525,0.0004179116,0.000244913,0.0001481005,0.0003289391],"domain_scores_gemma":[0.9992747,0.00002733157,0.0001656946,0.000383874,0.00008466892,0.0000637408],"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":[0.001769798,0.001362486,0.04984607,0.001167772,0.0007295329,0.00002312078,0.01454337,0.5345205,0.3635917,0.005217606,0.002201384,0.02502661],"study_design_scores_gemma":[0.006289343,0.001805661,0.1237384,0.0001383813,0.0001930294,0.0005677182,0.001609699,0.789544,0.02301667,0.0006096518,0.05119796,0.001289502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330824,0.0008437283,0.06383146,0.000377622,0.0001100097,0.0003738198,0.00007280306,0.00003354319,0.001274592],"genre_scores_gemma":[0.8621817,0.0002293989,0.1348944,0.00127148,0.0001876887,0.00003460087,0.0003953064,0.00004502812,0.0007603533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3405751,"threshold_uncertainty_score":0.6450104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007201868497908619,"score_gpt":0.2133350644116704,"score_spread":0.2061331959137617,"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."}}