{"id":"W4390663837","doi":"10.1038/s41467-023-44271-2","title":"Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Genentech; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Pfizer; Biogen; BioClinica; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; National Institute on Drug Abuse; Bristol-Myers Squibb; Eli Lilly and Company; Canadian Institutes of Health Research; National Science Foundation; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; U.S. Department of Health and Human Services; National Institute on Aging; Alzheimer's Association","keywords":"Cluster analysis; Computational biology; Artificial intelligence; Computer science; Gene; Pattern recognition (psychology); Biology; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009233977,0.000197684,0.0001196024,0.00006749751,0.000240113,0.0001584363,0.0005519272,0.0001327591,0.00000583094],"category_scores_gemma":[0.00007754397,0.000165264,0.00005462243,0.0001426082,0.0001424987,0.00001900381,0.0005869713,0.000605163,0.000005032113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001801782,"about_ca_system_score_gemma":0.0000575203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001286157,"about_ca_topic_score_gemma":0.0003472142,"domain_scores_codex":[0.999164,0.00007541142,0.0001893092,0.000259203,0.0001171255,0.0001949572],"domain_scores_gemma":[0.9986255,0.00004778489,0.00004849924,0.001106084,0.00006067538,0.0001114733],"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":[0.0004517517,0.0006167422,0.1821157,0.004211947,0.001417725,0.0001612238,0.004546344,0.04782081,0.4671075,0.001996415,0.0023487,0.2872052],"study_design_scores_gemma":[0.0003800367,0.00005170366,0.03605857,0.0002595593,0.0001395897,0.00007677243,0.0001108394,0.9215322,0.001596167,0.0000268955,0.03926604,0.0005016723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2718377,0.5632741,0.1576665,0.00443599,0.0004176028,0.0009739195,0.00009404872,0.0003008053,0.0009993821],"genre_scores_gemma":[0.902935,0.003145835,0.09303957,0.0004010063,0.00007357771,0.00003518396,0.0002565676,0.00004148872,0.00007180696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8737113,"threshold_uncertainty_score":0.673927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006602106932561773,"score_gpt":0.2687702865863528,"score_spread":0.2621681796537911,"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."}}