{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001869103,0.0001110722,0.0001718474,0.0003316917,0.0001601819,0.0004369429,0.0001994793,0.0002628834,0.0008861459],"category_scores_gemma":[0.0002634882,0.0001524518,0.0001950352,0.0002833347,0.0003207949,0.0002979485,0.0002475895,0.0005222176,0.0002722332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003125514,"about_ca_system_score_gemma":0.0001227238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007675528,"about_ca_topic_score_gemma":0.002467703,"domain_scores_codex":[0.999911,0.000009064883,0.000002368666,0.00004736108,0.0000182296,0.00001199882],"domain_scores_gemma":[0.9998023,0.00009728973,0.0000384546,0.00002320183,0.00001858302,0.00002011998],"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":[0.00006423616,0.00001010178,0.004386086,0.00001729633,0.00001567636,0.00003309708,0.00004458025,0.0006816974,0.9877819,0.001117228,0.000105793,0.005742368],"study_design_scores_gemma":[0.00002833839,0.0002655273,0.5087357,0.0000148945,0.0001303183,0.000786185,0.00044448,0.04124867,0.4293697,0.00973524,0.009189856,0.00005103372],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9423935,0.000997237,0.05249295,0.0001989106,0.00003426886,0.00002282665,0.0009560985,0.0001479055,0.00275631],"genre_scores_gemma":[0.9889637,0.0004498381,0.007677749,0.0001274336,0.00002464976,0.00002774076,0.0008078901,0.00005464107,0.001866381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008861459,"threshold_uncertainty_score":0.002964437,"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."}}