{"id":"W2052853820","doi":"10.1016/s1672-0229(08)60040-6","title":"How Do Variable Substitution Rates Influence Ka and Ks Calculations?","year":2009,"lang":"en","type":"article","venue":"Genomics Proteomics & Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Institute of Genetics and Developmental Biology, Chinese Academy of Sciences; Chinese Academy of Sciences; National Key Research and Development Program of China; Yale University","keywords":"Nonsynonymous substitution; Substitution (logic); Gamma distribution; Mutation rate; Mutation; Computation; Variable (mathematics); Trait; Mathematics; Selection (genetic algorithm); Novelty; Statistics; Algorithm; Biology; Computer science; Genetics; Artificial intelligence; Psychology; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002494127,0.0002674304,0.000223384,0.00009528016,0.0003199611,0.0002685533,0.0002170108,0.0002075659,0.000001519004],"category_scores_gemma":[0.0000993059,0.0002637993,0.00006120062,0.0001652642,0.0001323572,0.00001245589,0.0001451236,0.0001229941,0.000006778454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004240704,"about_ca_system_score_gemma":0.0001362354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005879481,"about_ca_topic_score_gemma":0.000003951008,"domain_scores_codex":[0.9988644,0.00002010388,0.0003715352,0.0002706981,0.0001118003,0.0003614218],"domain_scores_gemma":[0.9990765,0.00001091846,0.0002067087,0.0004148369,0.0001704027,0.0001205988],"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":[0.00008529227,0.00005085489,0.00250205,0.00006314951,0.0000988585,0.000001039409,0.0004559135,0.004326903,0.9685256,0.01807193,0.0002454921,0.005572852],"study_design_scores_gemma":[0.00614703,0.00239693,0.06117458,0.0001249844,0.0003476414,0.0003611653,0.00160481,0.04074292,0.5259709,0.03506862,0.3221534,0.003907015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716929,0.001070058,0.02519503,0.0004776217,0.0001046542,0.00065563,0.0000644385,0.00001165228,0.0007279873],"genre_scores_gemma":[0.8709869,0.001022033,0.127043,0.0005005479,0.0001277566,0.00002633873,0.00008380836,0.00001690245,0.0001926365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4425547,"threshold_uncertainty_score":0.9999814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007864974020594795,"score_gpt":0.2195346164330397,"score_spread":0.2116696424124449,"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."}}