{"id":"W2896982389","doi":"10.1371/journal.pone.0205295","title":"Combining multi-OMICs information to identify key-regulator genes for pleiotropic effect on fertility and production traits in beef cattle","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Beef Cattle Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Ontario Ministry of Agriculture, Food and Rural Affairs; Beef Farmers of Ontario; Ministry of Agriculture, Food and Rural Affairs; Ontario Centres of Excellence","keywords":"Biology; Pleiotropy; Quantitative trait locus; Gene; Genetics; Gene regulatory network; Regulator; Identification (biology); Candidate gene; Biotechnology; Computational biology; Selection (genetic algorithm); Phenotype; Gene expression","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":[0.001159159,0.0006336389,0.001406978,0.003557123,0.0004420971,0.001145321,0.0002970236,0.0003718134,0.001031463],"category_scores_gemma":[0.0007172679,0.0002543147,0.001720691,0.003556138,0.0001810234,0.0004379648,0.0007561017,0.0004594717,0.0001885485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005416455,"about_ca_system_score_gemma":0.0006254113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003114234,"about_ca_topic_score_gemma":0.004708297,"domain_scores_codex":[0.999526,0.00009763532,0.00004092457,0.0001585069,0.00009844633,0.00007859342],"domain_scores_gemma":[0.9994887,0.0002456245,0.0001104836,0.00003946825,0.00006487807,0.00005077523],"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":[0.001568423,0.0002957037,0.185019,0.00160554,0.002100135,0.0009258034,0.0005143296,0.008971342,0.7283065,0.0005815337,0.0009327869,0.06917894],"study_design_scores_gemma":[0.00006819525,0.0003086769,0.9464635,0.0001097615,0.001487886,0.0004573078,0.0004457912,0.02385071,0.02014441,0.001653661,0.004944497,0.00006557085],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9421019,0.006686946,0.02610764,0.0004236962,0.00004416915,0.00009972627,0.02307561,0.0002811518,0.001179131],"genre_scores_gemma":[0.8988068,0.002857746,0.06131405,0.0004357528,0.00005174182,0.0001772511,0.03539317,0.00009139712,0.0008720831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003557123,"threshold_uncertainty_score":0.006192207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02585331249796146,"score_gpt":0.2648212321984654,"score_spread":0.2389679197005039,"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."}}