{"id":"W3029193123","doi":"10.1101/2020.05.31.115741","title":"INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Medical Research Council; Genome Canada","keywords":"Interpretability; Classifier (UML); Support vector machine; Tuberculosis; Predictive testing; Test set; Drug resistant tuberculosis; Training set; Predictive value","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001318314,0.0009887593,0.0008361345,0.001133001,0.0003076439,0.0008841997,0.001408654,0.001527518,0.003603598],"category_scores_gemma":[0.006472348,0.0001790447,0.0006031684,0.0004836342,0.0004574567,0.0009610682,0.001043769,0.001048506,0.001253463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005805768,"about_ca_system_score_gemma":0.0007596666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002576302,"about_ca_topic_score_gemma":0.002725418,"domain_scores_codex":[0.9989797,0.0002916403,0.00006569937,0.0001774882,0.0003770225,0.0001083833],"domain_scores_gemma":[0.9983265,0.0009047357,0.0001786603,0.0002071852,0.0002857176,0.00009727701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001312914,0.000507184,0.01701711,0.0005608452,0.0002007282,0.0007601638,0.0002348965,0.2612084,0.03036711,0.008651392,0.03441504,0.6447642],"study_design_scores_gemma":[0.00003978781,0.0001171525,0.0009663663,0.00002448153,0.00001655455,0.0001209454,0.00003264147,0.9861902,0.005788756,0.005049413,0.001635857,0.00001787415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2122833,0.001742827,0.747601,0.001932917,0.0004938059,0.0003034762,0.004088455,0.02360348,0.00795083],"genre_scores_gemma":[0.7456518,0.0002908767,0.2451235,0.0008365111,0.000216843,0.0001773636,0.00451961,0.0004233697,0.002760204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003603598,"threshold_uncertainty_score":0.01205528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03280427305661016,"score_gpt":0.2954389930635088,"score_spread":0.2626347200068987,"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."}}