{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002830015,0.0002572433,0.0002450147,0.00005702283,0.0001170694,0.0001190953,0.0004065708,0.0002289902,0.00001179429],"category_scores_gemma":[0.00003967464,0.0002255442,0.0001631926,0.00008920644,0.00006687825,0.00004997123,0.0001526013,0.0001294764,0.00003800961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003148348,"about_ca_system_score_gemma":0.00006034605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000155417,"about_ca_topic_score_gemma":0.000008987007,"domain_scores_codex":[0.9987283,0.00002457989,0.0004796507,0.0002397258,0.0001515726,0.0003761935],"domain_scores_gemma":[0.9989345,0.000022473,0.0002255582,0.0005469025,0.0001183225,0.0001522407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004421528,0.0005757445,0.01035267,0.0003653023,0.0008882314,0.000002104192,0.01205284,0.02589823,0.9063955,0.009622977,0.001935769,0.03146845],"study_design_scores_gemma":[0.002555754,0.0003815152,0.0004967008,0.00002300825,0.0000517729,0.000006636547,0.006362132,0.8888485,0.09633284,0.0005407092,0.003751747,0.0006487275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3880215,0.00003952466,0.6097022,0.000008924712,0.0001416022,0.0005302186,0.00008784777,0.00002222224,0.001446001],"genre_scores_gemma":[0.5583916,0.00002374604,0.4392778,0.0004443627,0.00006453268,0.00003777031,0.001630119,0.00002449334,0.0001055627],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8629502,"threshold_uncertainty_score":0.9197427,"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."}}