{"id":"W2945801406","doi":"","title":"Inferring phenotypes from genotypes with machine learning : an application to the global problem of antibiotic resistance","year":2019,"lang":"en","type":"article","venue":"Corpus Université Laval (Université Laval)","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Université Laval","keywords":"Antibiotic resistance; Genotype; Computer science; Artificial intelligence; Resistance (ecology); Phenotype; Biology; Antibiotics; Machine learning; Genetics; Gene; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00255618,0.001090306,0.001388202,0.00157591,0.0006117338,0.001281719,0.001454394,0.001787022,0.001676598],"category_scores_gemma":[0.008758548,0.0004094098,0.001182682,0.001863307,0.0008021478,0.001161365,0.001246962,0.001568631,0.0003781086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009071135,"about_ca_system_score_gemma":0.001281618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01068406,"about_ca_topic_score_gemma":0.008542448,"domain_scores_codex":[0.9990464,0.0003937535,0.00006884992,0.0002627487,0.0001448003,0.00008340866],"domain_scores_gemma":[0.99444,0.004523955,0.0002629569,0.0003290425,0.0003293011,0.0001148117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000159602,0.0002069378,0.01038641,0.0001581066,0.0001390811,0.0002446897,0.0001972258,0.6834447,0.001674885,0.003274246,0.0016838,0.2984303],"study_design_scores_gemma":[0.00001308279,0.00003316156,0.001293629,0.00001210132,0.00001250269,0.0000432921,0.00004316459,0.9896053,0.0006303687,0.007692667,0.0006099531,0.0000107267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08647718,0.001259858,0.9071149,0.00121913,0.00009629416,0.0001253597,0.0003744854,0.001683556,0.001649162],"genre_scores_gemma":[0.4998645,0.000619817,0.4950587,0.0003018547,0.0001227623,0.0002061714,0.0008954732,0.0001850757,0.002745577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01068406,"threshold_uncertainty_score":0.02124369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00489898175148783,"score_gpt":0.1975144158092163,"score_spread":0.1926154340577285,"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."}}