{"id":"W2551917609","doi":"10.1101/067611","title":"DIABLO - an integrative, multi-omics, multivariate method for multi-group classification","year":2018,"lang":"en","type":"preprint","venue":"UWA Profiles and Research Repository (University of Western Australia)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prevention of Organ Failure; University of British Columbia","funders":"","keywords":"Omics; Biomarker discovery; Computer science; Multivariate statistics; Identification (biology); Data integration; Computational biology; Data mining; Bioinformatics; Machine learning; Biology; Proteomics; Ecology","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":[0.005701208,0.001398512,0.0012352,0.004413546,0.0008550921,0.002135445,0.002056782,0.001087929,0.004719265],"category_scores_gemma":[0.01247835,0.0005170809,0.002660728,0.003231077,0.0006203011,0.001289601,0.002920534,0.002783542,0.002327586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009645008,"about_ca_system_score_gemma":0.002749788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003473705,"about_ca_topic_score_gemma":0.005139287,"domain_scores_codex":[0.9978876,0.0008755741,0.0001312256,0.0004379745,0.0005349123,0.0001327887],"domain_scores_gemma":[0.9971172,0.001358275,0.0002624469,0.0005907686,0.000477649,0.000193689],"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":[0.0007856837,0.0005247661,0.0215271,0.0007132239,0.001452476,0.0002817482,0.0004318786,0.07004252,0.01139142,0.0277381,0.04397724,0.8211339],"study_design_scores_gemma":[0.0002153176,0.0001924112,0.008692853,0.0001132875,0.0002284946,0.0002695821,0.0001663556,0.8827891,0.004870216,0.06946815,0.03287685,0.0001174374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00340569,0.0001790355,0.9901379,0.0002292889,0.00005251025,0.000146464,0.001794308,0.003634893,0.000419761],"genre_scores_gemma":[0.05831569,0.0001821338,0.9298084,0.0003385464,0.0001027171,0.0008872085,0.007830716,0.0008245355,0.001709921],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005701208,"threshold_uncertainty_score":0.03015125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1350097401581828,"score_gpt":0.3890285775453768,"score_spread":0.2540188373871941,"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."}}