{"id":"W2950511618","doi":"10.1093/bioinformatics/bty387","title":"Indexed variation graphs for efficient and accurate resistome profiling","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Facilities Council; Department for Business, Energy and Industrial Strategy, UK Government; Public Health Agency of Canada; World Health Organization","keywords":"Resistome; Metagenomics; Profiling (computer programming); Computer science; Workflow; Data mining; Computational biology; Biology; Bioinformatics; Genome; Gene; Genetics; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001614278,0.00008703468,0.00007896952,0.000032954,0.0001431253,0.00002550378,0.00006430619,0.00006755802,8.357615e-7],"category_scores_gemma":[0.0000680607,0.00007658107,0.00003098705,0.00004687976,0.00006858069,7.976024e-7,0.00006881406,0.00001889324,0.0000029897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004931147,"about_ca_system_score_gemma":0.00002792274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001788687,"about_ca_topic_score_gemma":0.000003897777,"domain_scores_codex":[0.9995112,0.000006591697,0.0001829692,0.0001024211,0.0000521435,0.0001446354],"domain_scores_gemma":[0.99961,0.00001161324,0.00008677264,0.0001403894,0.0001155274,0.00003568402],"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":[0.0004215438,0.00009216509,0.004523241,0.0003702111,0.0003005248,2.578946e-7,0.003266927,0.0003346939,0.9684563,0.008488787,0.002515072,0.01123022],"study_design_scores_gemma":[0.006035609,0.003808096,0.1166472,0.00007896968,0.0002519436,0.00003313294,0.001915165,0.1475774,0.6194632,0.00485446,0.097538,0.001796828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835233,0.0002042644,0.01485251,0.00004457144,0.0001909425,0.0003276278,0.00004003938,0.000004714775,0.0008119989],"genre_scores_gemma":[0.9711736,0.00006456402,0.02837974,0.0001357335,0.0001241771,0.00002403521,0.00003025091,0.000008168886,0.00005968317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3489931,"threshold_uncertainty_score":0.3122885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506174008485551,"score_gpt":0.2494737906952323,"score_spread":0.2344120506103768,"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."}}