{"id":"W2940808208","doi":"10.1186/s13015-019-0139-6","title":"Reconciling multiple genes trees via segmental duplications and losses","year":2019,"lang":"en","type":"article","venue":"Algorithms for Molecular Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal; Université de Montréal; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Gene duplication; Segmental duplication; Tree (set theory); Genome; Gene; Set (abstract data type); Time complexity; Computer science; Computational biology; Biology; Gene family; Algorithm; Mathematics; Genetics; Combinatorics","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":[0.003101662,0.001333755,0.001766729,0.002872509,0.002102084,0.002346258,0.003126131,0.002569058,0.003466282],"category_scores_gemma":[0.01074823,0.0008115461,0.002546472,0.003498227,0.001273028,0.005292403,0.002923707,0.003240408,0.001416233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001651343,"about_ca_system_score_gemma":0.001645251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002559575,"about_ca_topic_score_gemma":0.00399346,"domain_scores_codex":[0.9979772,0.0005579354,0.0001230911,0.0008534029,0.0003142097,0.0001741876],"domain_scores_gemma":[0.9950722,0.002694464,0.0004570328,0.001223661,0.0003550139,0.0001975871],"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":[0.00147668,0.0003830373,0.03157804,0.001254444,0.0006575361,0.002057439,0.002493491,0.3837783,0.02379269,0.07134378,0.03797301,0.4432116],"study_design_scores_gemma":[0.0001594147,0.0001158779,0.004633347,0.0001154295,0.0002368743,0.00136045,0.001145032,0.7741045,0.01270072,0.1738282,0.03152061,0.00007948671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3378595,0.004067311,0.6317744,0.0042654,0.0003856922,0.0003536999,0.006424091,0.008707343,0.006162577],"genre_scores_gemma":[0.5072854,0.0007791675,0.4689094,0.0008857854,0.0001799677,0.0002445593,0.01707913,0.001275773,0.003360916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003466282,"threshold_uncertainty_score":0.01640338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218585346680486,"score_gpt":0.2636286312657858,"score_spread":0.251442777798981,"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."}}