{"id":"W3002979846","doi":"10.1038/s41564-019-0656-6","title":"Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing","year":2020,"lang":"en","type":"article","venue":"Nature Microbiology","topic":"Antibiotic Resistance in Bacteria","field":"Biochemistry, Genetics and Molecular Biology","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Institute of Allergy and Infectious Diseases; Programme Grants for Applied Research; Medical Research Council; Faculty of Arts and Sciences; U.S. Department of Health and Human Services; National Institutes of Health; Directorate for Biological Sciences; Rosetrees Trust; University of East Anglia; Oxford Nanopore Technologies; Biotechnology and Biological Sciences Research Council; Bill and Melinda Gates Foundation; David and Lucile Packard Foundation; Government of Canada; Canadian Institutes of Health Research; National Science Foundation; National Institute for Health and Care Research","keywords":"Antibiotic resistance; Typing; Inference; Antibiotics; Biology; Genetics; Computational biology; Resistance (ecology); Microbiology; Computer science; Artificial intelligence; Ecology","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":[0.0008186693,0.0004433268,0.0007195762,0.001640203,0.0003082505,0.0008027553,0.0006794322,0.0008848655,0.0009234991],"category_scores_gemma":[0.006687354,0.0004352756,0.0005925224,0.001161171,0.0003195809,0.0008123085,0.0009568995,0.000704253,0.0009445255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000351124,"about_ca_system_score_gemma":0.0004027055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002475163,"about_ca_topic_score_gemma":0.004435651,"domain_scores_codex":[0.9987935,0.0003382275,0.00009394308,0.0003753762,0.0003144115,0.00008453125],"domain_scores_gemma":[0.9976624,0.00118854,0.000451518,0.0003420562,0.0002508816,0.0001047449],"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":[0.001254583,0.0002118,0.1282891,0.0005414762,0.0002700266,0.0007083234,0.0006137916,0.02307902,0.6380702,0.00476336,0.00213608,0.2000623],"study_design_scores_gemma":[0.00009321022,0.0005392502,0.09768284,0.0001380447,0.0002358384,0.00267353,0.0004844866,0.4821188,0.3762971,0.01787275,0.02154039,0.0003237976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.554396,0.001300339,0.4316734,0.0003081934,0.0001401468,0.0001609644,0.004943598,0.002743033,0.00433442],"genre_scores_gemma":[0.7188146,0.0004102032,0.2757125,0.0001711632,0.00004068345,0.00007288317,0.003582546,0.0001768746,0.001018564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002475163,"threshold_uncertainty_score":0.004921496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006745591495306646,"score_gpt":0.2349925648085976,"score_spread":0.2282469733132909,"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."}}