{"id":"W3170411500","doi":"10.1101/2021.06.11.448022","title":"Prediction and Characterization of Disorder-Order Transition Regions in Proteins by Deep Learning","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Agency for Medical Research and Development","keywords":"Folding (DSP implementation); GRASP; Artificial intelligence; Intrinsically disordered proteins; Computer science; Machine learning; Chemistry; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006609159,0.0006713342,0.0005730382,0.001359464,0.00029636,0.0004248996,0.000701034,0.0006796392,0.0006690214],"category_scores_gemma":[0.001187137,0.0002843206,0.0005977038,0.0005738778,0.0005254761,0.0009081772,0.0006269568,0.0009794384,0.0002622658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006812931,"about_ca_system_score_gemma":0.0005668649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004257667,"about_ca_topic_score_gemma":0.004651301,"domain_scores_codex":[0.9998319,0.00003299139,0.00001089712,0.0000550298,0.00003595154,0.00003314412],"domain_scores_gemma":[0.9992906,0.0002607442,0.0001535698,0.00006956201,0.0001445704,0.00008091172],"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":[0.0004990039,0.000415806,0.02084613,0.0002665771,0.0001027543,0.0003117461,0.00009016653,0.8088098,0.06598531,0.004298863,0.003147712,0.09522612],"study_design_scores_gemma":[0.000001777691,0.000007616981,0.000361882,0.00000166724,0.000001955028,0.000004871782,0.000002585253,0.9972013,0.001866804,0.0005026134,0.00004505677,0.000001951985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6826538,0.001003028,0.3117579,0.0002810122,0.0000412947,0.00005625546,0.0005505254,0.002476786,0.001179444],"genre_scores_gemma":[0.9670215,0.0001512616,0.03158027,0.00006378966,0.0000101898,0.00002516235,0.000601492,0.00004897383,0.0004973128],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004257667,"threshold_uncertainty_score":0.008465767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004359597063594536,"score_gpt":0.1833838932425303,"score_spread":0.1790242961789358,"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."}}