{"id":"W4286716482","doi":"10.1038/s43856-022-00127-2","title":"A diagnostic classifier for gene expression-based identification of early Lyme disease","year":2022,"lang":"en","type":"article","venue":"Communications Medicine","topic":"Vector-borne infectious diseases","field":"Immunology and Microbiology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control","funders":"Office of Extramural Research, National Institutes of Health; National Heart, Lung, and Blood Institute; U.S. Department of Health and Human Services; National Institutes of Health; Global Lyme Alliance; Bay Area Lyme Foundation; Steven and Alexandra Cohen Foundation","keywords":"Lyme disease; Classifier (UML); Computational biology; Disease; Identification (biology); Gene; Biology; Artificial intelligence; Genetics; Computer science; Medicine; Virology; Pathology; Botany","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001009983,0.0006320698,0.0009183135,0.002003188,0.0004050294,0.0008868432,0.0006249865,0.000859066,0.001646471],"category_scores_gemma":[0.00220318,0.0001414823,0.0006215646,0.0009384937,0.0002418811,0.00032016,0.0003548062,0.000535062,0.001015883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005553206,"about_ca_system_score_gemma":0.0005695331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009813397,"about_ca_topic_score_gemma":0.0007970918,"domain_scores_codex":[0.9993254,0.000102746,0.00005998728,0.0002287203,0.0001895551,0.00009361007],"domain_scores_gemma":[0.9989541,0.0005193372,0.0001241848,0.00005579839,0.0002882536,0.0000582479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002289081,0.001605655,0.2764442,0.0008319917,0.0005199336,0.0009578362,0.0001940461,0.03140839,0.1955092,0.001479752,0.01589131,0.4728687],"study_design_scores_gemma":[0.0001412108,0.0008840166,0.1187979,0.0001265258,0.0002987827,0.001157745,0.0002431779,0.8071067,0.06154407,0.003553356,0.006072664,0.00007387224],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7925465,0.003406756,0.1850723,0.0009154388,0.0003698602,0.0004151171,0.009616022,0.003369393,0.004288555],"genre_scores_gemma":[0.9121274,0.0003235805,0.07832684,0.0002581507,0.00008716038,0.0003852691,0.007262577,0.00004123153,0.001187734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002003188,"threshold_uncertainty_score":0.005507946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349217861551722,"score_gpt":0.3010277285304937,"score_spread":0.2661059423753215,"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."}}