{"id":"W2277366394","doi":"10.1073/pnas.1506788112","title":"Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference","year":2015,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Inference; Computer science; Bayesian probability; Bayesian inference; Protein structure; Sequence (biology); Data mining; Computational biology; Artificial intelligence; Chemistry; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003751552,0.0001063855,0.0001243357,0.00002587104,0.0001002188,0.00002901562,0.001052693,0.00007889656,5.800861e-7],"category_scores_gemma":[0.0002888474,0.00006809131,0.00001904645,0.0001919793,0.0005119863,0.0000576702,0.0003348337,0.0001132413,7.61024e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001002201,"about_ca_system_score_gemma":0.00008640152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007154533,"about_ca_topic_score_gemma":8.791e-8,"domain_scores_codex":[0.9987587,0.000005758732,0.0001599754,0.0003081132,0.0006244712,0.0001429267],"domain_scores_gemma":[0.9994696,0.00001633676,0.0002585325,0.0000258788,0.0001775144,0.00005217798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003704782,0.00002266918,0.001156731,0.00003874497,0.0000157061,5.601288e-9,0.0000657385,0.0007301382,0.988999,0.007041414,0.001628635,0.0002641685],"study_design_scores_gemma":[0.0003976165,0.000218766,0.0001779338,0.00006857198,0.00001632935,0.000009046662,0.0001165639,0.07755604,0.8107293,0.110245,0.0002840234,0.0001808511],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997163,0.0001424004,0.0006142509,0.0002204608,0.000006248584,0.0002057302,0.0001022216,0.000006102535,0.001539593],"genre_scores_gemma":[0.9948279,0.000003271749,0.004947911,0.0000912631,0.00004069147,0.00000969024,0.000006805801,0.000005531104,0.00006689627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1782697,"threshold_uncertainty_score":0.2776683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03630136460292119,"score_gpt":0.3064858829378277,"score_spread":0.2701845183349065,"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."}}