{"id":"W3107667189","doi":"10.1101/2020.11.23.394296","title":"find-tfbs: a tool to identify functional non-coding variants associated with complex human traits using open chromatin maps and phased whole-genome sequences","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"NHLBI Division of Intramural Research; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; U.S. Department of Health and Human Services; National Institutes of Health; Fondation Institut de Cardiologie de Montréal; Institut de Cardiologie de Montréal","keywords":"DNA binding site; Biology; Computational biology; Genetics; Genome-wide association study; Genome; Gene; Human genome; Regulatory sequence; Transcription factor; Promoter; Single-nucleotide polymorphism; Gene expression; Genotype","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":[0.002750057,0.002715232,0.002137403,0.005358879,0.001277524,0.002033947,0.003111665,0.00187106,0.06251022],"category_scores_gemma":[0.01076197,0.001492017,0.003093529,0.003640635,0.0007300663,0.001135329,0.003755426,0.001724489,0.02330181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006386099,"about_ca_system_score_gemma":0.00174781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006457252,"about_ca_topic_score_gemma":0.01310702,"domain_scores_codex":[0.9987258,0.0002307847,0.0001098206,0.0005320749,0.0002805005,0.000121081],"domain_scores_gemma":[0.9959008,0.0027689,0.0003234087,0.0005797998,0.0001850894,0.0002419594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002183057,0.0001789809,0.05139392,0.00529912,0.002373987,0.001693289,0.0006596147,0.01124684,0.01537865,0.006386718,0.8412728,0.06193305],"study_design_scores_gemma":[0.004542778,0.000568889,0.06436535,0.001211592,0.00171256,0.003149622,0.0005190257,0.09514216,0.02715018,0.05976793,0.7413146,0.0005552737],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01438198,0.001390412,0.06090192,0.0004676128,0.00026765,0.0002718212,0.8040857,0.1147506,0.003482323],"genre_scores_gemma":[0.06311607,0.0009057029,0.1143149,0.000961668,0.0001542778,0.001707006,0.7918218,0.02356754,0.003451013],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06251022,"threshold_uncertainty_score":0.2091175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05742172693229689,"score_gpt":0.2946387240352734,"score_spread":0.2372169971029765,"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."}}