{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01171194,0.001651019,0.002241258,0.003719897,0.001132412,0.002636286,0.003237434,0.001638319,0.001041403],"category_scores_gemma":[0.0282294,0.00228823,0.002308086,0.002598361,0.002190214,0.003544322,0.00339802,0.002943287,0.0004972685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00189381,"about_ca_system_score_gemma":0.002364869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006624813,"about_ca_topic_score_gemma":0.01100548,"domain_scores_codex":[0.9950557,0.003364688,0.0002426121,0.0005149257,0.000731294,0.0000908029],"domain_scores_gemma":[0.980224,0.01513774,0.001355641,0.002265451,0.0007040731,0.0003132268],"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":[0.0001403222,0.00014857,0.004701296,0.0001834618,0.0003150258,0.0001014871,0.0001856191,0.9260396,0.001726701,0.02030389,0.0011422,0.04501178],"study_design_scores_gemma":[0.00001732313,0.00001071368,0.000182455,0.000009565036,0.00001364296,0.00001440077,0.00001298672,0.9628797,0.0003082028,0.03618326,0.0003510546,0.0000166638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01629655,0.000203785,0.9822116,0.0002826473,0.000008191432,0.00003906602,0.0001993989,0.0003970414,0.0003616462],"genre_scores_gemma":[0.2647977,0.0003751037,0.7319134,0.000300861,0.00007207399,0.0003169868,0.001596465,0.0001913836,0.0004360333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01171194,"threshold_uncertainty_score":0.06193942,"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."}}