{"id":"W2914061306","doi":"10.1002/pmic.201800458","title":"Improving MHC‐I Ligand Identifications from LC‐MS/MS Data by Incorporating Allelic Peptide Motifs","year":2019,"lang":"en","type":"letter","venue":"PROTEOMICS","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"False discovery rate; Major histocompatibility complex; Computational biology; MHC class I; CD8; Proteomics; Peptide; Biology; Computer science; Antigen; Immunology; Genetics; Biochemistry; Gene","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003361667,0.000478839,0.0004212259,0.00006652567,0.0001901848,0.0003823153,0.001699696,0.0009563682,0.00001683732],"category_scores_gemma":[0.0002148764,0.0004735488,0.0001331758,0.00008838572,0.00005806145,0.00003809445,0.001319207,0.0009742444,0.0001085327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003975926,"about_ca_system_score_gemma":0.0002338504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005850169,"about_ca_topic_score_gemma":0.00003501956,"domain_scores_codex":[0.9976292,0.00005698343,0.0007516708,0.0008770726,0.000251915,0.0004331538],"domain_scores_gemma":[0.9963039,0.00003157731,0.0008326261,0.002650821,0.0001300493,0.00005102039],"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":[0.0000107281,0.00002458635,0.0001932917,0.0001853195,0.0001713702,0.000001744137,0.00004744262,0.00001778024,0.2980305,0.000002110191,0.7006874,0.0006277263],"study_design_scores_gemma":[0.0008911812,0.0001296468,0.00009089073,0.0001308256,0.0001903344,0.00002118515,0.00009876897,0.01310636,0.1130448,0.0001922002,0.870839,0.001264842],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5775595,0.01180971,0.162485,0.2026002,0.003926279,0.01220134,0.02372978,0.0003417882,0.005346301],"genre_scores_gemma":[0.04042271,0.001852636,0.1313859,0.2248229,0.01801432,0.000855514,0.5531976,0.0009597886,0.02848869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5371369,"threshold_uncertainty_score":0.9997716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377755465020035,"score_gpt":0.2340909475018067,"score_spread":0.2103133928516063,"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."}}