{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008852636,0.0002900688,0.0001843984,0.00005317682,0.000067762,0.00003945419,0.0001472637,0.0007058061,0.001547294],"category_scores_gemma":[0.00001603683,0.0002542686,0.00006042357,0.000006908085,0.000124413,0.000005577227,0.0002281474,0.0003326907,0.00001708816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001234947,"about_ca_system_score_gemma":0.00007611064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003653754,"about_ca_topic_score_gemma":0.00002001716,"domain_scores_codex":[0.9990791,0.00001148435,0.0002870159,0.0003190904,0.0001505185,0.000152807],"domain_scores_gemma":[0.9992583,0.000003120099,0.0001766381,0.0004010467,0.00008423542,0.00007669406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009089466,0.00005476263,0.0002449283,0.003373375,0.002275949,0.00002434302,0.001060095,0.000122933,0.08752403,0.08351502,0.6029186,0.217977],"study_design_scores_gemma":[0.0007691438,0.0009009153,0.00007191954,0.0001765275,0.00009614153,0.0001484635,0.00002199494,0.005597313,0.005342142,0.03440706,0.951556,0.0009123952],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006884841,0.0004436735,0.002225231,0.00006766157,0.0001503049,0.0005567054,0.0003878302,0.00004933778,0.9892344],"genre_scores_gemma":[0.01146471,0.0001381558,0.01855175,0.0003190502,0.0008084978,0.00000929415,0.001587259,0.00008377099,0.9670375],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3486374,"threshold_uncertainty_score":0.9999909,"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."}}