{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002766699,0.0001783206,0.0003903573,0.0006203103,0.0002244093,0.0004489267,0.0001317752,0.0002044878,0.001209928],"category_scores_gemma":[0.0002783649,0.0001346115,0.0003108774,0.0009914752,0.0002073957,0.0002079973,0.0003128642,0.0002080509,0.0001991431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002275614,"about_ca_system_score_gemma":0.0001764494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001700481,"about_ca_topic_score_gemma":0.002927792,"domain_scores_codex":[0.9997904,0.00002408075,0.000007205138,0.00010331,0.00002880582,0.00004626677],"domain_scores_gemma":[0.999808,0.00005829514,0.00005977702,0.0000123479,0.00003346644,0.00002802839],"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":[0.0007650753,0.00004704401,0.2379155,0.000396898,0.000344984,0.0001604199,0.0006283991,0.001963509,0.7352254,0.0003291339,0.000795422,0.02142827],"study_design_scores_gemma":[0.000005543418,0.00009212204,0.9886045,0.00001482746,0.000100969,0.0001559694,0.0002198831,0.002767491,0.006551736,0.0002493459,0.001223023,0.00001447821],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909229,0.0009488362,0.002885745,0.00003883292,0.000005781464,0.000007213149,0.004624647,0.00005653855,0.0005095375],"genre_scores_gemma":[0.9886819,0.0003687319,0.002563366,0.00008616973,0.00001202664,0.00002890517,0.007386053,0.00004322049,0.0008295695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001700481,"threshold_uncertainty_score":0,"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."}}