{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001116461,0.0005751759,0.0008105467,0.0009977154,0.0006239331,0.001113891,0.001180985,0.0006263374,0.001280005],"category_scores_gemma":[0.004158089,0.0003435562,0.0006419592,0.001006591,0.0005966324,0.0009670938,0.000689332,0.0008986185,0.0003717375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009251058,"about_ca_system_score_gemma":0.001559323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003493089,"about_ca_topic_score_gemma":0.003769184,"domain_scores_codex":[0.9997106,0.00008511135,0.00001972749,0.00005945006,0.0000928305,0.00003220542],"domain_scores_gemma":[0.997772,0.001552111,0.0001362022,0.0001986811,0.000213996,0.0001269322],"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":[0.0001381826,0.000065582,0.005014921,0.0001407947,0.00005919531,0.0001046165,0.00004344622,0.9595749,0.002884079,0.01136919,0.001790912,0.01881411],"study_design_scores_gemma":[0.000005132335,0.000006823988,0.0002571916,0.000004380174,0.000003506978,0.00001108486,0.000004053275,0.9959142,0.001069909,0.002464947,0.0002557371,0.000003105245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6260303,0.001680115,0.3557641,0.0007443922,0.0001233884,0.0001040112,0.00288559,0.004205999,0.008462183],"genre_scores_gemma":[0.8173653,0.0008230561,0.1747496,0.0000771596,0.00004926971,0.0001787347,0.004839328,0.000355585,0.00156195],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003493089,"threshold_uncertainty_score":0.00694555,"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."}}