{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000357431,0.0001129804,0.0002101241,0.0001874085,0.0005972881,0.000004051802,0.0008081969,0.00004341729,0.0005808678],"category_scores_gemma":[0.001886122,0.0001070678,0.00008920695,0.0002546971,0.0005274753,0.00004442998,0.0002432683,0.0001765982,0.00003146036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007623246,"about_ca_system_score_gemma":0.0001748828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007709572,"about_ca_topic_score_gemma":0.000008009371,"domain_scores_codex":[0.9986892,0.0004283805,0.0004548218,0.0002019929,0.00006581414,0.0001597855],"domain_scores_gemma":[0.9954199,0.002282699,0.0003018881,0.001783931,0.000173253,0.00003834606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003539273,0.001389637,0.03527288,0.0000704236,0.0001454968,0.000001380428,0.0006592901,0.0001792872,0.9357382,0.003290155,0.02083012,0.002069202],"study_design_scores_gemma":[0.01407896,0.002125912,0.563351,0.0004151738,0.001831302,0.00002690925,0.001787523,0.001325408,0.2184983,0.006161597,0.1894244,0.0009734688],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9154888,0.04898237,0.01652225,0.01242407,0.001495518,0.002697994,0.001541276,0.000326301,0.0005214289],"genre_scores_gemma":[0.9957615,0.00009161681,0.0001385016,0.0003184086,0.0000255613,0.001773382,0.001499908,0.00002077831,0.0003703405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7172399,"threshold_uncertainty_score":0.6360095,"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."}}