{"id":"W2145270588","doi":"10.1186/1477-5956-11-s1-s5","title":"Predicting beta-turns in proteins using support vector machines with fractional polynomials","year":2013,"lang":"en","type":"article","venue":"Proteome Science","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China; Central South University; Ministry of Education of the People's Republic of China; City University of Hong Kong","keywords":"Support vector machine; Computer science; Stability (learning theory); Artificial intelligence; Protein secondary structure; Folding (DSP implementation); Machine learning; Logistic regression; Position (finance); Pattern recognition (psychology); Algorithm; Data mining; Mathematics; Chemistry","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":[0.001217185,0.001027211,0.000918565,0.001273621,0.0003520872,0.0005613008,0.0008236459,0.0007440633,0.000753471],"category_scores_gemma":[0.002899482,0.0002173404,0.000955624,0.0009807509,0.0002810664,0.0007509317,0.0004029089,0.0009315911,0.0003706706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005103761,"about_ca_system_score_gemma":0.0006459155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003923295,"about_ca_topic_score_gemma":0.002375523,"domain_scores_codex":[0.9994032,0.000175701,0.00004650342,0.0001239508,0.0001797643,0.00007090114],"domain_scores_gemma":[0.9981574,0.001103312,0.0002644973,0.00008852625,0.0003128001,0.0000733737],"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":[0.0002773707,0.000176565,0.007789185,0.0001168633,0.00009367777,0.0001519253,0.0000423714,0.7904276,0.00515787,0.0009552008,0.001663526,0.1931479],"study_design_scores_gemma":[0.000002197849,0.00001763774,0.000244458,0.000002273643,0.00000338622,0.000009116881,0.000003818333,0.9988183,0.0004632043,0.0003681817,0.00006521226,0.000002075077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2706489,0.002279531,0.7220402,0.0005875347,0.00009960371,0.00007294283,0.0003757066,0.002544781,0.001350759],"genre_scores_gemma":[0.9043409,0.0004456756,0.09371753,0.0001007344,0.00008733846,0.00004929699,0.0005882511,0.00003874298,0.0006316086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003923295,"threshold_uncertainty_score":0.007800937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01004224127476358,"score_gpt":0.2643495973709819,"score_spread":0.2543073560962183,"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."}}