{"id":"W4409645431","doi":"10.1093/gpbjnl/qzaf033","title":"TRAIT: A Comprehensive Database for T-cell Receptor–antigen Interactions","year":2025,"lang":"en","type":"article","venue":"Genomics Proteomics & Bioinformatics","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Zhejiang University; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"T-cell receptor; Antigen; Biology; Trait; Computational biology; T cell; Binding affinities; Chimeric antigen receptor; Database; Immunology; Receptor; Computer science; Immune system; Genetics","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":[0.001442509,0.002598355,0.002829984,0.006919431,0.0008521887,0.003343544,0.002739252,0.001994758,0.01191772],"category_scores_gemma":[0.005829381,0.0009945726,0.001958213,0.00900708,0.0003619634,0.002888806,0.003388538,0.001768143,0.01293242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008745385,"about_ca_system_score_gemma":0.002390374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003058461,"about_ca_topic_score_gemma":0.003840818,"domain_scores_codex":[0.9984587,0.0002406814,0.000354072,0.0004476915,0.0003411483,0.0001576404],"domain_scores_gemma":[0.9981566,0.0005965296,0.0003858756,0.0003550657,0.0002588794,0.0002470338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004078145,0.0004912747,0.0390233,0.02917268,0.002526331,0.003359997,0.0009138915,0.01491945,0.07472214,0.01934557,0.6053249,0.2061224],"study_design_scores_gemma":[0.00048727,0.0003158305,0.02451659,0.001312744,0.001143025,0.002646145,0.0003686489,0.017348,0.0170318,0.01597042,0.9185391,0.0003203531],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01823121,0.01433429,0.02783406,0.0003947594,0.0001604262,0.0001869918,0.9074697,0.02499425,0.006394272],"genre_scores_gemma":[0.02337102,0.004224213,0.02050779,0.0002673643,0.00003858321,0.0002567595,0.9496689,0.0009414057,0.0007239687],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01191772,"threshold_uncertainty_score":0.03986871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03631192317494432,"score_gpt":0.326807436567054,"score_spread":0.2904955133921097,"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."}}