{"id":"W4411150076","doi":"10.1158/2159-8290.cd-24-1488","title":"Federated Deep Learning Enables Cancer Subtyping by Proteomics","year":2025,"lang":"en","type":"article","venue":"Cancer Discovery","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Research Institute in Oncology and Hematology; McGill University; CancerCare Manitoba; Jewish General Hospital","funders":"National Cancer Institute; Agència de Gestió d'Ajuts Universitaris i de Recerca; Cancer Council Victoria; Cancer Council NSW; National Health and Medical Research Council; Horizon 2020 Framework Programme; CRIS Cancer Foundation; Medical Research Council; NSW Ministry of Health; Cancer Institute NSW; University of Sydney; Australian Cancer Research Foundation; State Government of Victoria; European Commission; Fondation du cancer du sein du Québec; Children's Medical Research; National Breast Cancer Foundation; David and Elaine Potter Foundation; AstraZeneca; Tour de Cure; U.S. Department of Health and Human Services","keywords":"Computer science; Generalizability theory; Subtyping; Replicate; Biomedicine; Machine learning; Artificial intelligence; Matching (statistics); Data mining; Bioinformatics; Medicine; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004069042,0.0006594188,0.0006970671,0.0006143671,0.0004931803,0.001016225,0.001504183,0.0008847656,0.0007158919],"category_scores_gemma":[0.00792863,0.000364575,0.0008441021,0.0006568982,0.0009418,0.001770609,0.002455252,0.001671967,0.0002154776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001473109,"about_ca_system_score_gemma":0.002265808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008049585,"about_ca_topic_score_gemma":0.01034488,"domain_scores_codex":[0.9988244,0.0004559045,0.00006821075,0.0003508072,0.0001682034,0.0001323674],"domain_scores_gemma":[0.9972363,0.001105201,0.0002702746,0.0008910458,0.0003363251,0.0001607992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005289867,0.0005304677,0.02878022,0.00006997034,0.0002500711,0.0003196114,0.0002015031,0.8155203,0.008165779,0.00468217,0.002699939,0.1382509],"study_design_scores_gemma":[0.00001357443,0.0000713785,0.001063877,0.000006407411,0.0000153092,0.00003622928,0.00003026184,0.9868768,0.003822295,0.007572118,0.0004836296,0.000008086111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3472279,0.000228869,0.6458516,0.001166685,0.00006667077,0.0001137687,0.000723506,0.003024816,0.001596288],"genre_scores_gemma":[0.9157547,0.00005919721,0.08201453,0.0003013648,0.0000174206,0.00008502347,0.0007208465,0.00004674653,0.001000103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008049585,"threshold_uncertainty_score":0.02151942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008372419420359248,"score_gpt":0.2838279326398147,"score_spread":0.2754555132194554,"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."}}