{"id":"W2909472027","doi":"10.1093/bioinformatics/bty1054","title":"DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1056,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prevention of Organ Failure; University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; National Health and Medical Research Council","keywords":"Bioconductor; Omics; Computer science; Computational biology; Benchmark (surveying); Identification (biology); Relevance (law); Data integration; Visualization; Data mining; Data science; Bioinformatics; Biology; Ecology; Cartography; Geography","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":[0.006976734,0.00199008,0.001341732,0.004109098,0.0006357391,0.00277389,0.002782492,0.0007569416,0.005470053],"category_scores_gemma":[0.01089978,0.0008304259,0.002236223,0.002695648,0.0008207423,0.001704565,0.004033125,0.002234583,0.001597216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087848,"about_ca_system_score_gemma":0.002560625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002578362,"about_ca_topic_score_gemma":0.005145956,"domain_scores_codex":[0.9984297,0.0005300827,0.0000767912,0.0004985304,0.0003867141,0.0000781725],"domain_scores_gemma":[0.9959786,0.002526325,0.0003618591,0.0004836219,0.0004070226,0.0002425011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001741533,0.0006720619,0.0675237,0.004683438,0.004591292,0.0008095721,0.0008151769,0.2110687,0.08997278,0.05632981,0.06772384,0.4940681],"study_design_scores_gemma":[0.0002170507,0.00033437,0.01576924,0.0002352611,0.0006343159,0.000516492,0.0002170918,0.8499641,0.02255083,0.06585111,0.04348295,0.0002271318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01272227,0.0007430803,0.9638133,0.0004445628,0.00008327961,0.0002187773,0.01039261,0.00970509,0.001876999],"genre_scores_gemma":[0.1033289,0.0007359275,0.8742003,0.0004782642,0.0001205715,0.0009810205,0.0171798,0.001818096,0.001157111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006976734,"threshold_uncertainty_score":0.03689694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0158927330930595,"score_gpt":0.2508353458635373,"score_spread":0.2349426127704778,"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."}}