{"id":"W2734882699","doi":"10.1109/tcbb.2017.2712695","title":"Evolutionary Model for the Statistical Divergence of Paralogous and Orthologous Gene Pairs Generated by Whole Genome Duplication and Speciation","year":2017,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Gene duplication; Genome; Divergence (linguistics); Biology; Genetic algorithm; Gene; Gene conversion; Genome evolution; Sequence (biology); Evolutionary biology; Computational biology; Permutation (music); Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007649391,0.0008457413,0.001738457,0.002820398,0.001149671,0.002374844,0.00513217,0.002844208,0.004022822],"category_scores_gemma":[0.01312138,0.0009284613,0.002483679,0.001763108,0.003044332,0.003698419,0.001996463,0.003384385,0.001270031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00211944,"about_ca_system_score_gemma":0.001003718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002172202,"about_ca_topic_score_gemma":0.001491064,"domain_scores_codex":[0.9979411,0.0006598462,0.00009622911,0.0006910554,0.0004239979,0.0001877524],"domain_scores_gemma":[0.9951755,0.003349988,0.0005819098,0.0003626956,0.0003113895,0.0002185348],"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":[0.0002387446,0.0001421407,0.01438887,0.0001964011,0.0002689861,0.0007763014,0.001137549,0.5801709,0.01226097,0.3655947,0.001326792,0.02349763],"study_design_scores_gemma":[0.00004921855,0.00009091783,0.003150309,0.00001811592,0.00004255751,0.0005481218,0.00007350277,0.8427007,0.0005524171,0.1515125,0.001194663,0.00006692707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1823479,0.0004466901,0.8118565,0.001230701,0.0000738294,0.0001292142,0.0003913614,0.0004587218,0.003065068],"genre_scores_gemma":[0.8741232,0.0007993946,0.1117255,0.0005929638,0.0001600063,0.0007796187,0.0008547943,0.000275818,0.01068854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007649391,"threshold_uncertainty_score":0.04045433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01931967658343894,"score_gpt":0.265033992598471,"score_spread":0.2457143160150321,"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."}}