{"id":"W2525586707","doi":"10.1101/045153","title":"Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; Université de Montréal; McGill University; Institute for Research in Immunology and Cancer; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; McGill University Health Centre; McGill University; Génome Québec; Compute Canada; Health Canada; Institut National de Santé Publique du Québec; Université de Montréal; Université Laval","keywords":"Computational biology; Computer science; Scalability; Genome; Identification (biology); Set (abstract data type); Machine learning; Artificial intelligence; Biology; Genetics; Gene","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":[0.003365298,0.001264426,0.001629773,0.002310923,0.0005469238,0.001824332,0.002290448,0.001403456,0.002562452],"category_scores_gemma":[0.01156518,0.000622947,0.001853972,0.002280355,0.0008883732,0.001683422,0.001627035,0.001199855,0.0006961656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077949,"about_ca_system_score_gemma":0.001246254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003094484,"about_ca_topic_score_gemma":0.003228714,"domain_scores_codex":[0.9979388,0.0008719059,0.00008710354,0.0006238443,0.0003906074,0.00008774803],"domain_scores_gemma":[0.9958082,0.002524981,0.0003060514,0.0008686268,0.0003939782,0.00009808754],"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":[0.0003011517,0.0001146843,0.006288468,0.0002047529,0.0003392915,0.000224028,0.00007372842,0.9075034,0.008370148,0.01534832,0.002250883,0.05898122],"study_design_scores_gemma":[0.00001672995,0.00002980178,0.0006122541,0.00000919338,0.00002900255,0.00004319986,0.000009765604,0.9792134,0.002782881,0.01620175,0.001035476,0.00001646789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07723853,0.0005509497,0.9141072,0.0004307154,0.00006594397,0.00005421072,0.001715343,0.00400099,0.001836224],"genre_scores_gemma":[0.5895897,0.000289053,0.4029081,0.0002453736,0.00006707995,0.0001913133,0.004914871,0.0007393308,0.001055207],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003365298,"threshold_uncertainty_score":0.01779759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02592510346564586,"score_gpt":0.2373427933331285,"score_spread":0.2114176898674826,"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."}}