{"id":"W2524436522","doi":"10.1101/078246","title":"Predicting Protein Thermostability Upon Mutation Using Molecular Dynamics Timeseries Data","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Western Canada Research Grid; Compute Canada","keywords":"Computer science; Artificial intelligence; Stability (learning theory); Mutation; Machine learning; Protein sequencing; Protein structure prediction; Protein structure; Computational biology; Biological system; Algorithm; Peptide sequence; Biology; Genetics; Biochemistry","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":[0.0008930687,0.0005577799,0.0003917266,0.001430172,0.0002244872,0.0004680244,0.0003944551,0.0005625968,0.0005754489],"category_scores_gemma":[0.003005057,0.0002097761,0.0005401724,0.0006661672,0.0001897646,0.0006049512,0.0002472734,0.0006277924,0.0002510369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000442762,"about_ca_system_score_gemma":0.0003021623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003041323,"about_ca_topic_score_gemma":0.0024208,"domain_scores_codex":[0.9998245,0.00003834354,0.00002280962,0.00005361405,0.00004731712,0.00001357576],"domain_scores_gemma":[0.9988426,0.0004943348,0.0002441058,0.0001238487,0.0002268106,0.00006825962],"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":[0.0009910371,0.0004827693,0.1070637,0.0002947446,0.0004628616,0.000509803,0.000194895,0.6496106,0.1436979,0.001805116,0.00235765,0.09252894],"study_design_scores_gemma":[0.000005182182,0.00004723281,0.007116485,0.000003053667,0.000009981288,0.00003468609,0.000007073354,0.9828734,0.009277656,0.0003972576,0.000215501,0.00001250902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9256105,0.0002095018,0.07043558,0.0001354592,0.00003065101,0.00004808098,0.001692953,0.001305373,0.0005319488],"genre_scores_gemma":[0.9697586,0.000111121,0.02768069,0.00001516153,0.0000084979,0.00004348077,0.002083712,0.00004463406,0.0002539257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003041323,"threshold_uncertainty_score":0.006047249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233933259409882,"score_gpt":0.2367158401326666,"score_spread":0.2243765075385677,"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."}}