{"id":"W2736429011","doi":"10.1093/bioinformatics/btx459","title":"Phylotyper: <i>in silico</i> predictor of gene subtypes","year":2017,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Subtyping; In silico; Python (programming language); Computational biology; R package; Phylogenetic tree; Whole genome sequencing; Genome; Data mining; Biology; Computer science; Gene; Genetics; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001065297,0.00009508789,0.0001361088,0.00002438823,0.00007921868,0.00001869084,0.0002771498,0.00007538884,0.000003446649],"category_scores_gemma":[0.00007231553,0.00008521891,0.00005344183,0.00001675055,0.0001044402,0.000001594588,0.000129153,0.00003194009,0.000006868006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004406935,"about_ca_system_score_gemma":0.00003870219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002303727,"about_ca_topic_score_gemma":0.00003447496,"domain_scores_codex":[0.9994414,0.000006192754,0.0002408598,0.0000888619,0.00007554809,0.0001471229],"domain_scores_gemma":[0.9992436,0.000005243791,0.0001682375,0.0005018764,0.00004686989,0.0000342046],"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":[0.00009340005,0.00009852732,0.3102502,0.0001396345,0.0001089993,0.000001487904,0.0005820166,0.0001256076,0.679953,0.0003472331,0.00161339,0.006686575],"study_design_scores_gemma":[0.001306454,0.0003841469,0.4202811,0.00002900213,0.00002605212,0.000007676481,0.0002308181,0.001004022,0.5481402,0.000167108,0.02809332,0.0003302153],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912868,0.0005604542,0.0001148084,0.00004412224,0.0001497245,0.0001124631,0.00005202651,0.000001682468,0.007677882],"genre_scores_gemma":[0.9952343,0.0004148127,0.004036423,0.0000584193,0.00008499112,0.000005703803,0.00001494628,0.000008293272,0.0001421729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1318128,"threshold_uncertainty_score":0.3475126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01176390060494871,"score_gpt":0.2319390014340548,"score_spread":0.2201751008291061,"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."}}