{"id":"W3155911001","doi":"10.1016/j.cell.2021.03.050","title":"Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease","year":2021,"lang":"en","type":"article","venue":"Cell","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":212,"is_retracted":false,"has_abstract":false,"ca_institutions":"Pacific Centre for Reproductive Medicine; University of British Columbia","funders":"National Human Genome Research Institute; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Institute of Mental Health; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; U.S. National Library of Medicine; National Institute on Aging; National Institutes of Health","keywords":"Biology; Gene; Trait; Quantitative trait locus; Genetics; Transcriptome; Expression quantitative trait loci; Genome-wide association study; Computational biology; Disease; Population; Gene expression; Genetic association; Long non-coding RNA; RNA; Genotype; Single-nucleotide polymorphism","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008240179,0.0001460923,0.0001385214,0.00004853731,0.000100517,0.00005290448,0.0001869663,0.0001112687,0.0001791906],"category_scores_gemma":[0.00002586977,0.000173789,0.00009589595,0.00017192,0.00002016925,0.000003358755,0.0001246528,0.0001160867,0.00006458397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004560564,"about_ca_system_score_gemma":0.0001202122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004070848,"about_ca_topic_score_gemma":0.0001343843,"domain_scores_codex":[0.9987999,0.00004982307,0.0001716571,0.0004494657,0.0002008698,0.0003282402],"domain_scores_gemma":[0.9990797,0.000005176617,0.00002808269,0.0004582079,0.0001112117,0.0003175996],"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":[0.00005967312,0.0000572352,0.00008254815,0.00005217334,0.00001466391,0.00005831258,0.00003953316,0.0006706623,0.9941886,0.00002361493,0.003380575,0.001372422],"study_design_scores_gemma":[0.0004542722,0.00005867526,0.002382268,0.00001865343,0.00002106954,0.000007467283,0.00003433237,0.0002810488,0.9466066,0.000050882,0.04987132,0.0002133624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3739209,0.001518712,0.6153516,0.001162065,0.0006831624,0.0007738827,0.0002783521,0.00004339629,0.006267977],"genre_scores_gemma":[0.9915186,0.0001167084,0.001747101,0.0006268303,0.0002132429,0.0000269072,0.001147123,0.00004749526,0.00455596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6175978,"threshold_uncertainty_score":0.7086909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01352210947446791,"score_gpt":0.2750978332159661,"score_spread":0.2615757237414982,"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."}}