{"id":"W4405892444","doi":"10.1038/s41467-024-54887-7","title":"Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Genentech","keywords":"Major histocompatibility complex; MHC class I; Immunotherapy; Computational biology; Computer science; Cancer immunotherapy; Immunology; Immunogenicity; Immune system; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001484736,0.0001633443,0.0001157867,0.00006134419,0.0001673729,0.0001243277,0.000594022,0.000220919,0.000006339743],"category_scores_gemma":[0.00002239231,0.0001289452,0.00007775516,0.0001877995,0.00005669418,0.00002564643,0.0001764069,0.0004293149,0.00000231119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003220954,"about_ca_system_score_gemma":0.0002368868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000516981,"about_ca_topic_score_gemma":0.0001621001,"domain_scores_codex":[0.9992828,0.00003987631,0.0002032925,0.0001883781,0.0001045024,0.0001811158],"domain_scores_gemma":[0.998729,0.0000179448,0.00006633588,0.001013996,0.0001344172,0.00003829274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002059086,0.0001647575,0.0006014205,0.0001273133,0.0008039783,7.435212e-7,0.002254543,0.003463316,0.9457459,0.005102653,0.01677183,0.02475762],"study_design_scores_gemma":[0.001472342,0.0004054099,0.001558538,0.0002063265,0.0001706997,0.00003802311,0.0009220532,0.5660813,0.1458674,0.0006067671,0.2817586,0.0009125356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2282627,0.5147343,0.2138126,0.01726271,0.0008654897,0.003126565,0.0003398456,0.0007602798,0.02083545],"genre_scores_gemma":[0.9535753,0.005819082,0.03894954,0.0003480985,0.00006875757,0.0002383609,0.000376693,0.00005339564,0.0005707862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7998785,"threshold_uncertainty_score":0.5258234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847778469510887,"score_gpt":0.3043565591511304,"score_spread":0.2858787744560216,"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."}}