{"id":"W4391565229","doi":"10.1186/s13015-023-00242-2","title":"Predicting horizontal gene transfers with perfect transfer networks","year":2024,"lang":"en","type":"article","venue":"Algorithms for Molecular Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Phylogenetic tree; Character (mathematics); Horizontal gene transfer; Inference; Phylogenetic network; Biology; Set (abstract data type); Similarity (geometry); Phylogenetics; Evolutionary biology; Gene; Genetics; Computational biology; Computer science; Artificial intelligence; Mathematics","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":[0.001142281,0.0006171645,0.0004822695,0.001725805,0.0006148911,0.001208716,0.001013684,0.001278001,0.002481401],"category_scores_gemma":[0.008670426,0.0004763597,0.0008317307,0.001245672,0.001041095,0.003121072,0.001010267,0.001108475,0.0003644144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389808,"about_ca_system_score_gemma":0.0007504617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002835599,"about_ca_topic_score_gemma":0.003327377,"domain_scores_codex":[0.9992318,0.0001989198,0.00005568004,0.0002990917,0.0001334908,0.00008098478],"domain_scores_gemma":[0.9920235,0.005632657,0.00117552,0.0005592628,0.0003828378,0.0002262878],"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":[0.0002150639,0.00007977712,0.01945819,0.0002358865,0.00009871699,0.0002293462,0.0001356396,0.8908937,0.002201301,0.01971346,0.001603335,0.06513554],"study_design_scores_gemma":[0.000009184082,0.00002147051,0.001516003,0.00001902598,0.00001656605,0.00007362511,0.00002975438,0.9492609,0.0007088741,0.04769734,0.000640074,0.000007171613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.324178,0.001363538,0.6673747,0.001064493,0.00003597628,0.00008880875,0.0020398,0.0009632229,0.002891421],"genre_scores_gemma":[0.8658165,0.0005171577,0.1296425,0.000125195,0.00007144317,0.00007501066,0.00248659,0.00008413383,0.001181561],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002835599,"threshold_uncertainty_score":0.01008373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007755730313516916,"score_gpt":0.2434699862928719,"score_spread":0.2357142559793549,"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."}}