{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006706627,0.0005777354,0.0005641273,0.0008066783,0.0004217831,0.000513127,0.0008972124,0.0007957706,0.001575443],"category_scores_gemma":[0.00305333,0.0006061814,0.0006423203,0.0007263432,0.000363758,0.001133205,0.0004899255,0.0008503809,0.0004994051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006142324,"about_ca_system_score_gemma":0.0007648213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005530004,"about_ca_topic_score_gemma":0.008059452,"domain_scores_codex":[0.9996707,0.000166054,0.00001693224,0.00006626856,0.00005289248,0.00002714603],"domain_scores_gemma":[0.9988801,0.0008724254,0.00007603884,0.00008290266,0.00005684085,0.00003171787],"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":[0.00009580923,0.00002912985,0.00120448,0.00003147703,0.00003347968,0.00005365561,0.00004062269,0.9666167,0.001118396,0.005095793,0.000418367,0.02526214],"study_design_scores_gemma":[0.000003827127,0.000006553225,0.00009851409,0.000002569586,0.00000471864,0.000006485085,0.000003566442,0.9951541,0.0002167274,0.004361517,0.0001379333,0.000003500995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0797281,0.0004880742,0.9163074,0.0002667976,0.00004491942,0.00006821877,0.0004948495,0.001222492,0.001379187],"genre_scores_gemma":[0.7063725,0.0005804228,0.2893071,0.0001116976,0.00005469391,0.0002433534,0.001369226,0.0001495365,0.001811421],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005530004,"threshold_uncertainty_score":0.01099563,"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."}}