{"id":"W2057771742","doi":"10.1142/s0219720006002314","title":"OPTIMALLY-CONNECTED HIDDEN MARKOV MODELS FOR PREDICTING MHC-BINDING PEPTIDES","year":2006,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Max-Planck-Gesellschaft; Genome Canada","keywords":"Hidden Markov model; Computation; Computer science; Human leukocyte antigen; Pattern recognition (psychology); Artificial intelligence; Merge (version control); Heuristic; Markov chain; Computational biology; Algorithm; Machine learning; Biology; Genetics; Antigen","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003646743,0.0001447007,0.0002310763,0.0001173286,0.0001291911,0.00005968059,0.0001484513,0.000126967,0.000002445671],"category_scores_gemma":[0.00006033335,0.0001160906,0.0001211768,0.00006002243,0.00005219895,0.0000368959,0.00007582019,0.00008757666,7.256054e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001263897,"about_ca_system_score_gemma":0.00008611564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003835446,"about_ca_topic_score_gemma":0.000001434072,"domain_scores_codex":[0.9988431,0.00001736227,0.000767248,0.00008354676,0.00009201493,0.0001967772],"domain_scores_gemma":[0.9988937,0.00008287901,0.0005807391,0.00007624421,0.0003142477,0.00005214364],"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":[0.003613962,0.001052989,0.03521939,0.002032983,0.003341936,0.000009859443,0.002802706,0.4660527,0.1151376,0.1180273,0.03122634,0.2214823],"study_design_scores_gemma":[0.002999616,0.001634471,0.002519268,0.00006986845,0.00008356909,0.0004787957,0.0008474761,0.9620091,0.00208716,0.02416348,0.002714321,0.0003928595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7965097,0.0005509042,0.2015086,0.0001887357,0.0001318105,0.000174619,0.00006055202,0.000006255749,0.0008688658],"genre_scores_gemma":[0.8145117,0.00008093124,0.1847038,0.0001084202,0.0002228517,0.000005097611,0.000313294,0.00001023988,0.00004369978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4959565,"threshold_uncertainty_score":0.4734036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01180921237253446,"score_gpt":0.2336836803964509,"score_spread":0.2218744680239164,"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."}}