{"id":"W2950763275","doi":"10.1101/213264","title":"The C-terminal extension landscape of naturally presented HLA-I ligands","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ontario Ministry of Economic Development and Innovation; European Federation of Pharmaceutical Industries and Associations; Novartis Pharma; Wellcome Trust; Ministero dello Sviluppo Economico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Diamond Light Source; Genome Canada; Pfizer","keywords":"Epitope; Human leukocyte antigen; Computational biology; Biology; Allele; Extension (predicate logic); HLA-A; Genetics; Chemistry; Antigen; Computer science; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005277153,0.0003993029,0.0003882151,0.00006834509,0.0003468948,0.000239933,0.001088456,0.0005297809,0.000003650677],"category_scores_gemma":[0.0003284339,0.0002976306,0.0002280429,0.0000705964,0.0001364723,0.00001256057,0.001165946,0.0004438305,0.000006616945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000171307,"about_ca_system_score_gemma":0.0003364302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001820716,"about_ca_topic_score_gemma":0.000002693388,"domain_scores_codex":[0.9982843,0.00004225352,0.0005276118,0.0004884677,0.0002674533,0.0003898466],"domain_scores_gemma":[0.9964111,0.00002200855,0.0007538859,0.002160758,0.0005457224,0.0001065428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001578604,0.00006930461,0.002376996,0.0002854623,0.0002497279,0.0000099657,0.000007426012,0.00003154223,0.9942797,0.00009683652,0.002419612,0.00001557409],"study_design_scores_gemma":[0.0008100452,0.0001910269,0.08480213,0.000345683,0.0001305967,2.353661e-7,0.00001134324,0.001557976,0.8925215,0.000003032323,0.01896485,0.0006615717],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921362,0.006124853,0.0001257468,0.0002136107,0.0008135759,0.0004320275,0.00008312894,0.00003203614,0.00003877234],"genre_scores_gemma":[0.9966326,0.002065215,0.0006711518,0.00004358862,0.0004085202,0.00005603303,0.000003148573,0.00006146383,0.00005832765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1017582,"threshold_uncertainty_score":0.9999476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118211661443075,"score_gpt":0.2278646381077703,"score_spread":0.2160434719634628,"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."}}