{"id":"W2129179457","doi":"10.1093/bioinformatics/btt716","title":"Sequence-based Gaussian network model for protein dynamics","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Sequence (biology); Protein structure prediction; Computer science; Gaussian; Protein function; Gaussian network model; Protein structure; Biological system; Algorithm; Statistical physics; Computational biology; Physics; Biology; Chemistry; Computational chemistry; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001389838,0.001012594,0.001092274,0.001065812,0.0005673844,0.0008104769,0.002413824,0.001753708,0.00302527],"category_scores_gemma":[0.004596511,0.0003859543,0.0008765691,0.001514426,0.001333674,0.002243923,0.001000899,0.001560387,0.001103051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001981333,"about_ca_system_score_gemma":0.001251001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01719814,"about_ca_topic_score_gemma":0.009165014,"domain_scores_codex":[0.9993562,0.0002582509,0.00001814597,0.0001614797,0.0001340025,0.00007195352],"domain_scores_gemma":[0.9985453,0.000809398,0.0002034634,0.00009296676,0.0002543603,0.00009458382],"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":[0.00003608083,0.0000188845,0.000599484,0.0000358996,0.00001650118,0.00005351668,0.00003180328,0.9641966,0.0006316478,0.02974224,0.0009175817,0.003719754],"study_design_scores_gemma":[0.000002222016,0.000003391855,0.00005034562,0.000001642824,0.000001620941,0.000006555479,0.000001369258,0.993068,0.00004783198,0.006604413,0.0002096428,0.000002955025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04358204,0.0007800904,0.9494045,0.001011842,0.00009000394,0.00006543313,0.0008127709,0.0005789808,0.003674291],"genre_scores_gemma":[0.8423799,0.002174972,0.1346096,0.000528016,0.0002141641,0.0005780524,0.002591407,0.0003481964,0.01657568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01719814,"threshold_uncertainty_score":0.03419608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01194801079776317,"score_gpt":0.2330714269190659,"score_spread":0.2211234161213028,"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."}}