{"id":"W4250246104","doi":"10.1002/pmic.201700319","title":"Deep Omics","year":2017,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Omics; Computational biology; Proteomics; Biology; Computer science; Bioinformatics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001055915,0.0013202,0.001213618,0.001922186,0.0007774605,0.004081643,0.00125686,0.001611755,0.01573447],"category_scores_gemma":[0.002543274,0.0004957861,0.001277437,0.002788302,0.0006585289,0.003231979,0.002661124,0.002442282,0.01246408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001343263,"about_ca_system_score_gemma":0.001777239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003062782,"about_ca_topic_score_gemma":0.004182075,"domain_scores_codex":[0.9990095,0.0001152171,0.00006079661,0.0002987225,0.0003867471,0.0001289861],"domain_scores_gemma":[0.999218,0.0001462453,0.0000937306,0.0002303231,0.0002080557,0.0001036224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007918956,0.0001820677,0.01408322,0.003540779,0.0007794399,0.0007513138,0.0003260031,0.01064077,0.1211038,0.1171558,0.3295485,0.4010964],"study_design_scores_gemma":[0.00006263071,0.00007664372,0.007795272,0.000649567,0.0002163053,0.0008347115,0.0001445435,0.02810351,0.03166051,0.1090911,0.8212531,0.0001121078],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03404321,0.06927657,0.5371837,0.02076648,0.005872504,0.0003250916,0.1833556,0.03117345,0.1180033],"genre_scores_gemma":[0.2455733,0.06338718,0.370217,0.02161998,0.002701208,0.0009335235,0.2388575,0.004625779,0.05208454],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01573447,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009658559691194536,"score_gpt":0.2380986445498923,"score_spread":0.2284400848586978,"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."}}