{"id":"W4290998961","doi":"10.3390/genes13081430","title":"An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs","year":2022,"lang":"en","type":"article","venue":"Genes","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Genomic selection; Biology; Heritability; Quantitative trait locus; Bubalus; Trait; Genomic information; Selection (genetic algorithm); Best linear unbiased prediction; Genetics; Genomics; Biotechnology; Dairy cattle; Genome; Computational biology; Statistics; Single-nucleotide polymorphism; Computer science; Mathematics; Machine learning; Gene; Genotype","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":[0.001575824,0.001039648,0.000990072,0.0009642742,0.0003578828,0.0004377404,0.001072698,0.0004869284,0.002115931],"category_scores_gemma":[0.001642805,0.0003788858,0.0008528747,0.0007552314,0.0002855514,0.0004634446,0.00107838,0.0006904138,0.0003165953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004323507,"about_ca_system_score_gemma":0.0009985908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008156504,"about_ca_topic_score_gemma":0.007998169,"domain_scores_codex":[0.9995891,0.0001181517,0.00001592832,0.0001604017,0.00007835036,0.0000379853],"domain_scores_gemma":[0.9994412,0.0003553027,0.00004156012,0.00002975605,0.000105605,0.00002662218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006821761,0.000324606,0.03929725,0.0003078794,0.0006791978,0.0004337375,0.0002234417,0.4556685,0.03031391,0.003005428,0.002496661,0.4665672],"study_design_scores_gemma":[0.00004457671,0.00012233,0.005785379,0.00001499309,0.0001556732,0.00007501993,0.0000286554,0.9886383,0.002065444,0.002075686,0.0009740781,0.00001976228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1496711,0.001079522,0.8439212,0.0002939117,0.00003348623,0.0001062086,0.0009270732,0.002567535,0.001400039],"genre_scores_gemma":[0.604082,0.0004776597,0.3906561,0.0003200503,0.00007896986,0.0002270828,0.00266649,0.0001523161,0.001339291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008156504,"threshold_uncertainty_score":0.01621807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009262314765395173,"score_gpt":0.2260906547144091,"score_spread":0.2168283399490139,"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."}}