{"id":"W3005774144","doi":"10.1101/2020.02.17.951863","title":"Extraction of Protein Dynamics Information Hidden in Cryogenic Electron Microscopy Maps Using Deep Learning","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Cryo-electron microscopy; Molecular dynamics; Structural biology; Computer science; Nanotechnology; Macromolecule; Biological system; Allosteric regulation; Artificial intelligence; Chemistry; Physics; Biophysics; Materials science; Biology; Computational chemistry","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":[0.0004175175,0.0006533107,0.0004073843,0.0009167485,0.0002310694,0.0006642001,0.0006167731,0.0007556882,0.0005866393],"category_scores_gemma":[0.0008590029,0.0003407315,0.0004149562,0.0005955435,0.0005200338,0.0009371753,0.0007943261,0.000960045,0.0002949757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005268453,"about_ca_system_score_gemma":0.0006142805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001872658,"about_ca_topic_score_gemma":0.001521926,"domain_scores_codex":[0.9999037,0.00001890858,0.000004709641,0.00002989826,0.00002445847,0.00001836005],"domain_scores_gemma":[0.9996927,0.0001059117,0.000045782,0.0000686639,0.0000604711,0.00002657362],"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":[0.0002525139,0.0002709441,0.00483112,0.0002511443,0.0001115578,0.0002731405,0.0001186346,0.6194267,0.1232974,0.0145945,0.004452823,0.2321196],"study_design_scores_gemma":[0.000002409232,0.000005487176,0.0003164035,0.000003012953,0.000002408853,0.00001227904,0.000005506724,0.9881352,0.007673061,0.003458226,0.0003813393,0.000004667243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1907017,0.0004988028,0.8042091,0.0004773751,0.00004103863,0.00003451208,0.0006639097,0.002233846,0.001139876],"genre_scores_gemma":[0.7144747,0.0005639907,0.28091,0.0001691052,0.00003998436,0.00006797029,0.002051527,0.0001852953,0.001537446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001872658,"threshold_uncertainty_score":0.003822565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006692820409038233,"score_gpt":0.2730769633061026,"score_spread":0.2663841428970644,"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."}}