{"id":"W4243639316","doi":"10.3410/f.736064114.793562214","title":"Faculty Opinions recommendation of A genetics-led approach defines the drug target landscape of 30 immune-related traits.","year":2019,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biomedical Research Council; European Federation of Pharmaceutical Industries and Associations; Eesti Teadusagentuur; Ministero dello Sviluppo Economico; Genome Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; Ontario Ministry of Economic Development and Innovation; Novartis Pharma; NIHR Oxford Biomedical Research Centre; Wellcome Trust; Merck KGaA; National Institute for Health and Care Research; Pfizer","keywords":"Prioritization; Computational biology; Genome-wide association study; Biology; Precision medicine; Drug discovery; Disease; Selection (genetic algorithm); Genome; CRISPR; Genetics; Bioinformatics; Gene; Computer science; Medicine; Single-nucleotide polymorphism; Machine learning; Genotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001825732,0.001270304,0.0009234722,0.002683533,0.0005213214,0.001787958,0.001868005,0.001549784,0.06151604],"category_scores_gemma":[0.008009228,0.0004554223,0.001063193,0.004228358,0.0003027522,0.0007808699,0.001332229,0.001375951,0.05121398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00146349,"about_ca_system_score_gemma":0.003330494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01231008,"about_ca_topic_score_gemma":0.03843704,"domain_scores_codex":[0.9987804,0.0002061515,0.0001373872,0.000361559,0.0003861079,0.000128327],"domain_scores_gemma":[0.9964167,0.001085806,0.0005401469,0.0007082385,0.0007840458,0.0004650672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002849123,0.00007856107,0.01295314,0.002102864,0.0001867254,0.0001127568,0.00003717543,0.00112004,0.001815711,0.001603664,0.9629729,0.01673155],"study_design_scores_gemma":[0.0002960635,0.00004625249,0.01765833,0.0003048993,0.0001124211,0.0001518648,0.00005391517,0.001983328,0.002214331,0.001806275,0.9753416,0.00003068451],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006813176,0.0001269945,0.0004223289,0.0002471971,0.0000251478,0.00003307382,0.9953191,0.0005953759,0.0025496],"genre_scores_gemma":[0.002579411,0.0001419324,0.001329835,0.0001714101,0.00001152409,0.00008763267,0.9943836,0.0000806609,0.001214032],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06151604,"threshold_uncertainty_score":0.2057917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469177172752814,"score_gpt":0.2909149335897352,"score_spread":0.2762231618622071,"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."}}