{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002218588,0.002267469,0.001434882,0.001970314,0.0009259255,0.001632013,0.001964368,0.0009570335,0.02652017],"category_scores_gemma":[0.007063805,0.0008895039,0.002188804,0.001555239,0.0005020251,0.001169954,0.001647091,0.001558487,0.02496164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007211438,"about_ca_system_score_gemma":0.001803853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002263566,"about_ca_topic_score_gemma":0.002824701,"domain_scores_codex":[0.9988359,0.0002441182,0.00008604526,0.0004153445,0.0002781355,0.0001404804],"domain_scores_gemma":[0.9976628,0.0009415725,0.0004567103,0.0004294625,0.0003081296,0.0002013866],"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":[0.00307111,0.0002768083,0.05686176,0.00317464,0.0009879916,0.0008635201,0.0005233733,0.01849665,0.06940157,0.006971559,0.7395085,0.09986252],"study_design_scores_gemma":[0.0009162819,0.000785102,0.06130625,0.0005829,0.0008120163,0.002374313,0.0003558917,0.2937915,0.155707,0.02130428,0.461538,0.0005264154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.04849316,0.0005797063,0.2464327,0.00129916,0.0003568107,0.0004949259,0.365438,0.3251282,0.0117773],"genre_scores_gemma":[0.144016,0.000428646,0.3101175,0.001101802,0.000135644,0.001271279,0.4879005,0.04835862,0.006670144],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02652017,"threshold_uncertainty_score":0.08871877,"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."}}