{"id":"W4415423483","doi":"10.1016/j.immuno.2025.100062","title":"Machine learning in AIRR diagnostics: Advances and applications","year":2025,"lang":"en","type":"article","venue":"ImmunoInformatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome Canada; Simon Fraser University","funders":"U.S. National Library of Medicine; National Institute of Allergy and Infectious Diseases; Deutsche Forschungsgemeinschaft","keywords":"Focus (optics); State (computer science); Training set; Repertoire; Active learning (machine learning)","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":[0.00616576,0.0009735173,0.001681156,0.001927234,0.0003961582,0.002896552,0.001407769,0.002088166,0.002528197],"category_scores_gemma":[0.009053065,0.0005157207,0.0008713283,0.002134165,0.001456204,0.002650378,0.001332042,0.003745924,0.001515509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001161266,"about_ca_system_score_gemma":0.001222522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376655,"about_ca_topic_score_gemma":0.001117653,"domain_scores_codex":[0.9979483,0.0009044184,0.0001330092,0.0003590923,0.0005641648,0.00009107972],"domain_scores_gemma":[0.9929749,0.005176428,0.0003073572,0.0003429448,0.001025838,0.0001724948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001539148,0.0001461488,0.005025081,0.004120348,0.0002565131,0.0002543301,0.000219236,0.02858784,0.003704296,0.05732765,0.02417035,0.8760343],"study_design_scores_gemma":[0.00006458269,0.0004935563,0.00661841,0.005087165,0.0002633252,0.001035768,0.0003976892,0.2030964,0.01049355,0.2868539,0.4852934,0.0003021841],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.008037708,0.7464814,0.2136198,0.02023362,0.001420549,0.00009185082,0.0004831954,0.0006692758,0.0089626],"genre_scores_gemma":[0.1202682,0.7207054,0.1451861,0.003908789,0.004587228,0.0001913634,0.001057731,0.0001801721,0.003914937],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00616576,"threshold_uncertainty_score":0.03260803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002161965810614259,"score_gpt":0.2037635460223367,"score_spread":0.2016015802117225,"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."}}