{"id":"W2042427414","doi":"10.1145/2494444.2494459","title":"Protein structural class prediction using predicted secondary structure and hydropathy profile","year":2013,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Computer science; Sequence (biology); Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Domain (mathematical analysis); Protein secondary structure; Protein structure prediction; Machine learning; Folding (DSP implementation); Data mining; Protein folding; Protein structure; Mathematics; Biology; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00005279393,0.0001680663,0.0001117033,0.00003933155,0.0001151492,0.00006449802,0.0001023799,0.0001999304,0.0005079607],"category_scores_gemma":[0.00005402765,0.0001352059,0.00002859904,0.00005810945,0.00007738909,0.00001955889,0.0001325186,0.0002049525,0.000004611116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001296118,"about_ca_system_score_gemma":0.00005334378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004644466,"about_ca_topic_score_gemma":0.000005674807,"domain_scores_codex":[0.9991651,0.00004480287,0.0002282171,0.0002246962,0.0001264272,0.0002107311],"domain_scores_gemma":[0.999492,0.000003214773,0.00009901074,0.000237014,0.00008070157,0.00008805948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002334678,0.000004720459,0.01248067,0.000103448,0.00004237285,5.200095e-7,0.0001015136,0.0002020374,0.9815556,0.00008329881,0.001141221,0.004261221],"study_design_scores_gemma":[0.001486296,0.0007205778,0.08067912,0.00004615732,0.00003783046,0.0002496871,0.0002083177,0.5545543,0.3559945,0.0004761631,0.004956891,0.0005902107],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957964,0.00005561785,0.001069623,0.00005728673,0.00009435252,0.0006469689,0.0001018006,0.00005009513,0.002127826],"genre_scores_gemma":[0.9819959,0.000002463037,0.01654327,0.0001318419,0.0001784896,0.00001874431,0.0003995669,0.00002100612,0.000708733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6255612,"threshold_uncertainty_score":0.5561813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003555813333943347,"score_gpt":0.2090870783872556,"score_spread":0.2055312650533123,"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."}}