{"id":"W3211646893","doi":"10.1016/j.xgen.2021.100033","title":"CanDIG: Federated network across Canada for multi-omic and health data discovery and analysis","year":2021,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; SickKids Foundation; Hospital for Sick Children; McGill Genome Centre; University of Waterloo; Vector Institute; Ontario Genomics; Canada's Michael Smith Genome Sciences Centre; Providence Health Care; Provincial Health Services Authority; University of British Columbia; Zymeworks (Canada); Princess Margaret Cancer Centre; Institute of Cancer Research; Ontario Institute for Cancer Research; Université de Sherbrooke; University of Toronto; University Health Network","funders":"","keywords":"Genomics; Data science; Alliance; Big data; Key (lock); Computer science; World Wide Web; Genome; Data mining; Biology; Geography; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.006685073,0.001540201,0.001006636,0.004920021,0.004938517,0.00639778,0.004954265,0.001180442,0.01381934],"category_scores_gemma":[0.0130193,0.0007478245,0.001276087,0.009660622,0.001748707,0.004089258,0.007743514,0.002363349,0.005302813],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02976124,"about_ca_system_score_gemma":0.08292992,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9074046,"about_ca_topic_score_gemma":0.8793902,"domain_scores_codex":[0.9956454,0.0004984131,0.0002282944,0.0008195626,0.001927852,0.0008805189],"domain_scores_gemma":[0.9877903,0.001222386,0.000407085,0.002642015,0.005806827,0.002131346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001322954,0.0001064623,0.01113664,0.0005808182,0.000321584,0.0004358573,0.001029362,0.01195554,0.005663631,0.1166787,0.6921488,0.1586196],"study_design_scores_gemma":[0.0002487057,0.00005391025,0.008385706,0.0003688108,0.0001267625,0.0002403063,0.0007881704,0.05709161,0.007002907,0.05130821,0.8741192,0.0002656379],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01951945,0.004038453,0.4990783,0.01479994,0.001264971,0.001984571,0.225312,0.1380867,0.09591559],"genre_scores_gemma":[0.1265783,0.003233073,0.476929,0.004154036,0.0002138422,0.001401833,0.3485374,0.007550859,0.03140168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9702387,"threshold_uncertainty_score":0.2159339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02850129202030238,"score_gpt":0.2802589828201911,"score_spread":0.2517576907998887,"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."}}