{"id":"W4317869953","doi":"10.1093/bioinformatics/btad055","title":"Binding peptide generation for MHC Class I proteins with deep reinforcement learning","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"National Science Foundation","keywords":"Reinforcement learning; Computational biology; Class (philosophy); Computer science; Peptide; MHC class I; Artificial intelligence; Major histocompatibility complex; Chemistry; Biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007596832,0.0007415313,0.0005403915,0.0002821441,0.0002448971,0.0004773644,0.001036092,0.0008777196,0.002535391],"category_scores_gemma":[0.001546116,0.0003735178,0.000510939,0.0002149547,0.0005476878,0.0004842434,0.000720449,0.001158544,0.0005699663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009033228,"about_ca_system_score_gemma":0.0008288003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001898633,"about_ca_topic_score_gemma":0.001690646,"domain_scores_codex":[0.999756,0.00005232701,0.00001149392,0.00007074364,0.00006500952,0.00004444105],"domain_scores_gemma":[0.9995105,0.0002550711,0.00005948415,0.00004801236,0.00007775654,0.00004915689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001424536,0.0001631533,0.001922547,0.000157972,0.00004343662,0.0001473919,0.000039618,0.9184281,0.01902272,0.004799617,0.002303068,0.05283],"study_design_scores_gemma":[0.00001139692,0.00002146529,0.00006748948,0.000003140135,0.000003553481,0.00001577029,0.000001867691,0.9953766,0.002866178,0.001278415,0.0003513531,0.000002779502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.175636,0.0007507225,0.8114438,0.0005621526,0.000140744,0.0001852253,0.0004346484,0.005290924,0.005555864],"genre_scores_gemma":[0.7702042,0.0002067642,0.2248754,0.0004211812,0.00003592112,0.0002713576,0.0007131336,0.0003029852,0.002968997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002535391,"threshold_uncertainty_score":0.008481741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448110864336796,"score_gpt":0.2395257250399956,"score_spread":0.2150446163966277,"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."}}