{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001293369,0.00009367416,0.00008067242,0.000009999398,0.0002747406,0.0001134946,0.0003870791,0.0001271057,0.000007848301],"category_scores_gemma":[0.00004805894,0.00008985534,0.00005616369,0.000007111271,0.00007668098,0.000004035365,0.000227156,0.00007780863,0.00003789664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007176622,"about_ca_system_score_gemma":0.00003110628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007433134,"about_ca_topic_score_gemma":0.00002024914,"domain_scores_codex":[0.9994953,0.000005950117,0.0001283537,0.0001404553,0.00004747862,0.0001824551],"domain_scores_gemma":[0.999108,0.000001544203,0.0001287316,0.0006687762,0.00003325643,0.00005972551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005244496,0.0002006196,0.02338021,0.0001480068,0.000365352,0.00001278253,0.0004410507,0.001864431,0.7265908,0.015618,0.01518198,0.2156723],"study_design_scores_gemma":[0.004165856,0.0006811676,0.01659834,0.00004443388,0.00006574151,0.0001115859,0.000156091,0.07378691,0.552536,0.02514956,0.324791,0.001913372],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7339737,0.000444018,0.2285122,0.0007379501,0.000793282,0.0007022719,0.00001876015,0.00002595473,0.03479182],"genre_scores_gemma":[0.9709221,0.0001773646,0.02711518,0.0003429172,0.0005048782,0.00002420875,0.00004538735,0.00002002153,0.000847934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.309609,"threshold_uncertainty_score":0.3664194,"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."}}