{"id":"W2462204796","doi":"10.1016/j.ympev.2016.07.001","title":"PhyPA: Phylogenetic method with pairwise sequence alignment outperforms likelihood methods in phylogenetics involving highly diverged sequences","year":2016,"lang":"en","type":"article","venue":"Molecular Phylogenetics and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Phylogenetic tree; Pairwise comparison; Multiple sequence alignment; Biology; Sequence alignment; Phylogenetics; Sequence (biology); Tree (set theory); Alignment-free sequence analysis; Computational biology; Algorithm; Computer science; Genetics; Mathematics; Artificial intelligence; Gene; Combinatorics; Peptide sequence","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":[0.007051889,0.001550794,0.001581624,0.001744639,0.001396811,0.002683081,0.002632796,0.002961646,0.008168011],"category_scores_gemma":[0.01866517,0.001058612,0.001739394,0.002140302,0.001287277,0.004249213,0.003031653,0.00418528,0.004180747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005650556,"about_ca_system_score_gemma":0.001691475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001294668,"about_ca_topic_score_gemma":0.001261627,"domain_scores_codex":[0.9955761,0.002269763,0.0002165259,0.000878436,0.0008431502,0.0002160642],"domain_scores_gemma":[0.9930748,0.004815183,0.0004539796,0.000877549,0.0004920173,0.0002864473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001396789,0.000430163,0.01464325,0.00178849,0.0009988683,0.001061573,0.00111011,0.1491955,0.06554454,0.08205806,0.03776896,0.6440037],"study_design_scores_gemma":[0.0002015693,0.0003707859,0.005284895,0.0002405496,0.0001833924,0.0009572232,0.0001796841,0.8095154,0.04194719,0.07179858,0.06904089,0.0002797528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03134119,0.002118152,0.9371447,0.001247603,0.0003634588,0.0001090795,0.0007081481,0.02258619,0.004381441],"genre_scores_gemma":[0.1110912,0.0009419823,0.8790284,0.0004352529,0.0001270494,0.000315691,0.001124586,0.004937655,0.001998121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008168011,"threshold_uncertainty_score":0.03729439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01401484049449976,"score_gpt":0.2758326871836884,"score_spread":0.2618178466891886,"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."}}