{"id":"W2849436992","doi":"10.1007/978-3-032-18842-7_8","title":"Hidden Markov Models and Protein Secondary Structure Prediction","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hidden Markov model; Viterbi algorithm; Forward algorithm; Computer science; Stochastic matrix; Algorithm; Sequence (biology); Pattern recognition (psychology); Artificial intelligence; Markov model; Hidden semi-Markov model; Maximum-entropy Markov model; Markov chain; Variable-order Markov model; Machine learning; Biology; Genetics","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":[0.0005314595,0.001036679,0.0008996016,0.0006180799,0.0002354681,0.001006644,0.0010496,0.001048487,0.01006353],"category_scores_gemma":[0.001660006,0.0006020745,0.0005925676,0.001309495,0.0006632181,0.002118793,0.0006900327,0.001773907,0.008127626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005883849,"about_ca_system_score_gemma":0.0006422343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001395251,"about_ca_topic_score_gemma":0.001539465,"domain_scores_codex":[0.9996737,0.00007258443,0.00001407954,0.00006391275,0.0001589747,0.00001677282],"domain_scores_gemma":[0.9994212,0.0004267493,0.00002696393,0.00005645265,0.00005711351,0.00001162459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005186401,0.00007355717,0.0002851281,0.0005439225,0.00006586558,0.0001018528,0.00008969369,0.09516312,0.00251476,0.2281162,0.09830038,0.5746937],"study_design_scores_gemma":[0.000009889167,0.00002732701,0.0003480573,0.0001905475,0.00002381416,0.0001506494,0.00002149521,0.2264172,0.002543926,0.5995708,0.1706543,0.00004197198],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003215882,0.06114465,0.8776333,0.002614751,0.002060254,0.00003949191,0.0008961583,0.002911954,0.04948342],"genre_scores_gemma":[0.1125764,0.1092129,0.5150056,0.001451968,0.003905989,0.0003245887,0.006544718,0.002138906,0.2488389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01006353,"threshold_uncertainty_score":0.03366584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006094340806673071,"score_gpt":0.2086834743429656,"score_spread":0.2025891335362925,"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."}}