{"id":"W2895908163","doi":"10.1093/bioinformatics/bty883","title":"Landscape of multi-tissue global gene expression reveals the regulatory signatures of feed efficiency in beef cattle","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Agriculture and Agri-Food Canada; Alberta Livestock and Meat Agency; Alberta Agriculture and Forestry","keywords":"Transcriptome; Biology; Residual feed intake; Gene; Feed conversion ratio; Rumen; Gene expression; Beef cattle; Phenotype; Genetics; Regulator gene; Gene expression profiling; Trait; Computational biology; Body weight; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001815103,0.0001037729,0.0001386653,0.00002353278,0.00003888447,0.000004639439,0.0002801233,0.0001453593,0.00001409663],"category_scores_gemma":[0.00006916328,0.0000716288,0.00003978678,0.0001110008,0.0002173029,0.000003535848,0.0001069752,0.00004767624,0.00000369374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005399244,"about_ca_system_score_gemma":0.00005965791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008287755,"about_ca_topic_score_gemma":0.00001409712,"domain_scores_codex":[0.999216,0.00003251146,0.0003619642,0.00009789842,0.0001402003,0.0001514943],"domain_scores_gemma":[0.9993112,0.00001419385,0.0001875158,0.0003634917,0.00008907334,0.00003451078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001852212,0.0003155875,0.01968905,0.0002717367,0.00004670224,1.9304e-7,0.002739975,0.002829256,0.9600663,0.001344439,0.005806123,0.006705443],"study_design_scores_gemma":[0.0007224894,0.0004985794,0.2308373,0.00005947246,0.00001444781,0.000004274038,0.0004348058,0.0003372279,0.7660228,0.0002090602,0.0007124562,0.000147154],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883413,0.001055276,0.008826963,0.00001605802,0.0001498936,0.0002116264,0.00005508603,0.000004430434,0.001339342],"genre_scores_gemma":[0.9469737,0.00001566952,0.05276205,0.00004894526,0.00007395146,0.000004020389,0.00002474657,0.000005355786,0.00009160116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2111482,"threshold_uncertainty_score":0.2920938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009546474809135003,"score_gpt":0.2493989742368527,"score_spread":0.2398524994277177,"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."}}