{"id":"W2997828579","doi":"10.1186/s12859-019-3054-4","title":"Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection","year":2019,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Identification (biology); Computational biology; Feature selection; Whole genome sequencing; Biology; Selection (genetic algorithm); DNA microarray; Genetics; Genome; Artificial intelligence; Bioinformatics; Computer science; Gene; Gene expression","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":[0.00009305196,0.0001924252,0.0001669457,0.00004154843,0.0001272848,0.00009529856,0.0004861325,0.0001726434,0.00001142526],"category_scores_gemma":[0.00003741147,0.0001927616,0.00005221765,0.0001170293,0.00005045822,0.000009703509,0.0002845694,0.00008934517,0.00005450951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003365608,"about_ca_system_score_gemma":0.0001102301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003106812,"about_ca_topic_score_gemma":0.000246133,"domain_scores_codex":[0.9989098,0.000029576,0.0003385207,0.0003408814,0.0001377068,0.0002435102],"domain_scores_gemma":[0.9987552,0.00001239196,0.0002441483,0.0008273934,0.0001082179,0.00005260227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003063679,0.00001396719,0.01082403,0.00008168784,0.00005462867,1.958167e-7,0.0001433303,0.001927595,0.9861483,0.000003791751,0.0004389201,0.0003329263],"study_design_scores_gemma":[0.002354993,0.0002037607,0.2510841,0.0001305195,0.0002782622,0.00005768889,0.0007106978,0.4213065,0.1016891,0.0001195511,0.2199904,0.00207441],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358213,0.001051427,0.06000894,0.00004097966,0.0004787053,0.0003796576,0.002100873,0.00001049378,0.0001076115],"genre_scores_gemma":[0.7429659,0.0003349344,0.2516517,0.0001961413,0.0003517354,0.000005464879,0.003533629,0.0000376594,0.0009228229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8844592,"threshold_uncertainty_score":0.7860591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03449597150938384,"score_gpt":0.2599799649302015,"score_spread":0.2254839934208177,"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."}}