{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002556558,0.0001421588,0.0002418419,0.000006798607,0.0002781731,0.0001548549,0.0001393762,0.0000916167,0.000001430676],"category_scores_gemma":[0.00000825867,0.0001460642,0.00003695461,0.00008747179,0.00003900519,0.00000615398,0.000397108,0.00006564952,1.528483e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000453256,"about_ca_system_score_gemma":0.0006586713,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0180526,"about_ca_topic_score_gemma":0.6103959,"domain_scores_codex":[0.9989428,0.0000196545,0.0002573768,0.0003818792,0.00004066085,0.0003576306],"domain_scores_gemma":[0.9992644,0.0000136968,0.0001183675,0.0004302237,0.00004137201,0.0001319597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00107658,0.0005842469,0.1380995,0.002219223,0.01166964,0.00007769093,0.00400279,0.0801513,0.08244538,0.0004123548,0.5011959,0.1780654],"study_design_scores_gemma":[0.005705964,0.0002858103,0.02220479,0.00003346173,0.0007371802,0.00008117153,0.003288472,0.5214612,0.01147364,0.0001008319,0.4328906,0.001736833],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8730864,0.01765838,0.1059989,0.0005036589,0.0003141306,0.0003374663,0.002038115,0.000005563432,0.00005741951],"genre_scores_gemma":[0.9702356,0.004147049,0.01266942,0.002831993,0.0003118402,0.000004450103,0.008633722,0.00003040926,0.001135463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5923433,"threshold_uncertainty_score":0.9884863,"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."}}