{"id":"W2783632698","doi":"10.1002/cpps.28","title":"Computational Prediction of Intrinsic Disorder in Proteins","year":2017,"lang":"en","type":"article","venue":"Current Protocols in Protein Science","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Tokyo; China Scholarship Council; National Science Foundation","keywords":"Intrinsically disordered proteins; Computer science; Computational model; Field (mathematics); Artificial intelligence; Machine learning; Biology; Mathematics; Biophysics","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":[0.0007165524,0.0001335298,0.0001516047,0.0001537181,0.0001777501,0.00007522795,0.0008388147,0.00007978942,0.000004540403],"category_scores_gemma":[0.0005347108,0.0001242913,0.00003580783,0.0002199241,0.0007604437,0.00004751432,0.0003811423,0.0001720561,0.000001452836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004179865,"about_ca_system_score_gemma":0.0004066422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003271307,"about_ca_topic_score_gemma":0.00006392647,"domain_scores_codex":[0.9984995,0.00004171466,0.0003625239,0.0004596803,0.0003597537,0.000276829],"domain_scores_gemma":[0.9989173,0.000003814609,0.0002668589,0.0006123396,0.0001454031,0.00005425867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003192467,0.0006300245,0.4202952,0.0004398345,0.000006170278,0.000003817943,0.0001724379,0.002536496,0.3880163,0.009029221,0.00001691207,0.1785343],"study_design_scores_gemma":[0.004275385,0.0008850301,0.7151856,0.001259534,0.000003296933,0.000008956445,0.00003560886,0.007480527,0.2382994,0.02685661,0.005188773,0.0005212086],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9386727,0.00009105544,0.009744441,0.00007621263,0.0001217514,0.05074438,0.00003099311,0.000009455868,0.0005090724],"genre_scores_gemma":[0.962914,0.00000365889,0.003353954,0.000005456528,0.00006333682,0.03362031,0.00001262388,0.000007916801,0.00001870118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2948904,"threshold_uncertainty_score":0.506845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220204207170383,"score_gpt":0.341153796437156,"score_spread":0.3189517543654521,"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."}}