{"id":"W2010753155","doi":"10.1073/pnas.0910915107","title":"Mutation-selection models of coding sequence evolution with site-heterogeneous amino acid fitness profiles","year":2010,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":172,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computational biology; Population; Codon usage bias; Molecular evolution; Genetics; Selection (genetic algorithm); Context (archaeology); Coding region; Biology; Probabilistic logic; Statistical model; Phylogenetic tree; Amino acid; Gene; Computer science; Artificial intelligence; Genome","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":[0.003313567,0.0007011506,0.00112962,0.001051922,0.0005714333,0.001421113,0.002225995,0.001518441,0.001305924],"category_scores_gemma":[0.009639122,0.0005236457,0.001195774,0.001137252,0.002085479,0.002285008,0.001153711,0.001492125,0.000376103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163544,"about_ca_system_score_gemma":0.0006673768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003927134,"about_ca_topic_score_gemma":0.002698823,"domain_scores_codex":[0.9988396,0.0005449505,0.00004513703,0.0002984254,0.0001497988,0.000122167],"domain_scores_gemma":[0.996547,0.002245395,0.0005826146,0.0002884828,0.0001742252,0.0001622221],"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":[0.00004838068,0.00003210136,0.004810143,0.00004176124,0.00007686065,0.0002215759,0.0002406213,0.8094618,0.00409463,0.1740636,0.0002682966,0.006640171],"study_design_scores_gemma":[0.000009398105,0.00001853791,0.00119471,0.000003799837,0.00001026776,0.00006306241,0.0000157549,0.9339268,0.0001571456,0.06435152,0.0002331098,0.00001595695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.245227,0.0003677638,0.7510024,0.0007508615,0.00003163302,0.00004717524,0.0001745899,0.0001975693,0.002200938],"genre_scores_gemma":[0.9632575,0.0003517167,0.03225559,0.0001166651,0.00004433534,0.000133497,0.0001953134,0.00006331816,0.003582123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003927134,"threshold_uncertainty_score":0.01752406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02758974253658054,"score_gpt":0.271018641350869,"score_spread":0.2434288988142885,"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."}}