{"id":"W3104530972","doi":"10.1101/2020.11.07.372136","title":"Predicting drug resistance in <i>M. tuberculosis</i> using a Long-term Recurrent Convolutional Network","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Foreign, Commonwealth and Development Office; European and Developing Countries Clinical Trials Partnership; European Commission; Medical Research Council; Genome Canada","keywords":"Feature (linguistics); Convolutional neural network; Preprocessor; Pipeline (software); Representation (politics); Drug resistance; Task (project management); Feature learning","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":[0.0004576225,0.0009704465,0.0004348628,0.0006591229,0.0002656056,0.000630205,0.001025457,0.0008759483,0.001885511],"category_scores_gemma":[0.001922336,0.0003415367,0.0007576712,0.0006460869,0.0002003442,0.0006931029,0.0005440252,0.0009056039,0.001294991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008726972,"about_ca_system_score_gemma":0.0006861074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0264172,"about_ca_topic_score_gemma":0.03463389,"domain_scores_codex":[0.9997734,0.00003385272,0.00001127971,0.00009017583,0.0000409899,0.0000502638],"domain_scores_gemma":[0.9994763,0.0001901189,0.000073681,0.00007868317,0.0001392297,0.000042075],"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":[0.0009201585,0.0004711824,0.0594259,0.0004559341,0.0004198581,0.0004971597,0.00007352395,0.6744275,0.0446384,0.001988769,0.03535926,0.1813224],"study_design_scores_gemma":[0.000009733122,0.00004069877,0.004316554,0.0000140402,0.00003083181,0.00003690564,0.000008720097,0.9883397,0.005455725,0.0007932716,0.0009426615,0.00001113866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7856687,0.002594698,0.162983,0.002157653,0.0002354506,0.0001030432,0.02453371,0.01514074,0.006582939],"genre_scores_gemma":[0.9168431,0.0004846996,0.04592408,0.0003692629,0.00007406467,0.00007011272,0.03218557,0.0001993457,0.003849752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0264172,"threshold_uncertainty_score":0.05252683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03268786965436298,"score_gpt":0.2885778135071281,"score_spread":0.2558899438527651,"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."}}