{"id":"W2170912504","doi":"10.1109/cibcb.2005.1594947","title":"Biological Sequence Prediction using General Fuzzy Automata","year":2005,"lang":"en","type":"article","venue":"","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Hidden Markov model; Sequence (biology); Formalism (music); Computer science; Fuzzy logic; Artificial intelligence; Protein sequencing; Sequence analysis; Automaton; Markov chain; Machine learning; Pattern recognition (psychology); Data mining; Peptide sequence; 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":[0.0007257728,0.0004355428,0.0005787461,0.0007134193,0.0005623083,0.000771065,0.0008453719,0.0006875179,0.001267452],"category_scores_gemma":[0.003248538,0.0002019478,0.001113492,0.0004379824,0.0009840014,0.00158268,0.0006279725,0.0007160038,0.0003368935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008166467,"about_ca_system_score_gemma":0.0006315879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004367177,"about_ca_topic_score_gemma":0.003376837,"domain_scores_codex":[0.9995607,0.0001139588,0.00004269589,0.0001301553,0.0001200294,0.00003251271],"domain_scores_gemma":[0.9988385,0.0007279008,0.00007366573,0.0001226253,0.0001932541,0.00004401014],"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":[0.0001974462,0.00006444797,0.004174066,0.0002046266,0.00009071043,0.000394576,0.000390541,0.7067272,0.02203686,0.1350603,0.001453465,0.1292058],"study_design_scores_gemma":[0.000006245491,0.00002376303,0.0002393855,0.00001367111,0.00001169774,0.00004767497,0.00001811206,0.941522,0.001937174,0.05503052,0.001135619,0.00001400688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04734267,0.0002353029,0.9488955,0.0001905481,0.00005295857,0.0000399782,0.0001289052,0.0006311593,0.002482983],"genre_scores_gemma":[0.6479918,0.0004427003,0.3488065,0.0001092068,0.00004606769,0.0001213815,0.0003751184,0.00005998689,0.002047258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004367177,"threshold_uncertainty_score":0.008683503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03842663137413094,"score_gpt":0.2844399369289454,"score_spread":0.2460133055548144,"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."}}