{"id":"W1965012204","doi":"10.1155/2013/870372","title":"Predicting<i>β</i>-Turns in Protein Using Kernel Logistic Regression","year":2013,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Central South University; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Support vector machine; Artificial intelligence; Kernel (algebra); Computer science; Logistic regression; Machine learning; Algorithm; Pattern recognition (psychology); Probabilistic logic; Regression; Mathematics; Statistics; Combinatorics","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":[0.0008132052,0.0001121194,0.00008640368,0.0003076296,0.00006942655,0.00009498346,0.0004674613,0.0001493059,0.000248855],"category_scores_gemma":[0.001371131,0.00009239541,0.00004009015,0.0002121791,0.0001799377,0.00001494899,0.0003952958,0.0003497293,0.00009209258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009578744,"about_ca_system_score_gemma":0.0001130557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004609782,"about_ca_topic_score_gemma":0.0000369634,"domain_scores_codex":[0.9983485,0.0001641766,0.0002835264,0.0002484634,0.0005943717,0.0003609711],"domain_scores_gemma":[0.9992121,0.00003362116,0.00008384405,0.0002515369,0.0003202033,0.00009862571],"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":[0.0000903744,0.0001581396,0.067615,0.0000752378,0.00004480086,0.00001075893,0.0001699828,0.0002808151,0.922623,0.0003554542,0.006112028,0.002464436],"study_design_scores_gemma":[0.004815777,0.001154098,0.1494931,0.001038385,0.000009433677,0.0001720852,0.001024275,0.5160918,0.1444349,0.004347083,0.1762318,0.00118721],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920171,0.00008219344,0.0008041649,0.0008238807,0.0003497705,0.0004958294,0.00001490081,0.0000165294,0.005395584],"genre_scores_gemma":[0.991147,0.00002246605,0.005512467,0.00005244693,0.0004534357,0.0000834623,0.00015034,0.0000178485,0.002560571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.778188,"threshold_uncertainty_score":0.3767775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07403111163272218,"score_gpt":0.4091610444742174,"score_spread":0.3351299328414952,"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."}}