{"id":"W2003464575","doi":"10.1007/s00726-010-0721-1","title":"iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content","year":2010,"lang":"en","type":"article","venue":"Amino Acids","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Web server; Computer science; In silico; Sequence (biology); Protein structure prediction; Similarity (geometry); Structural similarity; Class (philosophy); Data mining; Protein secondary structure; Artificial intelligence; Computational biology; Protein structure; Biology; The Internet; Image (mathematics); Genetics; Biochemistry; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001037374,0.0001493152,0.000144494,0.00003252508,0.00005495668,0.00002416699,0.0001375228,0.0002608359,0.0000402595],"category_scores_gemma":[0.0001702849,0.0001207799,0.00003610931,0.00005484425,0.0001011666,0.00001566119,0.00006155614,0.0002466016,3.115427e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005876927,"about_ca_system_score_gemma":0.00009029973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000289763,"about_ca_topic_score_gemma":0.0002144882,"domain_scores_codex":[0.9993262,0.00002364341,0.0002374523,0.0001914613,0.00006514756,0.0001560415],"domain_scores_gemma":[0.999272,0.000006552601,0.0001383712,0.0002931101,0.0002241244,0.00006583006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000142164,0.000008553755,0.006629258,0.00006667705,0.00003924999,9.290403e-8,0.00005223119,0.000002202195,0.9846578,0.000243175,0.0001602108,0.007998421],"study_design_scores_gemma":[0.002615948,0.002554977,0.08615383,0.00002304353,0.00005762171,0.0000419957,0.0001855798,0.02360455,0.8424288,0.0002255865,0.04176164,0.0003464387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984162,0.0000502411,0.0002200331,0.00004266833,0.0002714743,0.0004738633,0.0004027602,0.00001881793,0.0001039592],"genre_scores_gemma":[0.9900547,0.000004296709,0.008160963,0.0000880029,0.0001254747,0.00001354841,0.001195815,0.00001905975,0.0003381039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.142229,"threshold_uncertainty_score":0.4925262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008275257855806557,"score_gpt":0.2324697708959622,"score_spread":0.2241945130401556,"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."}}