{"id":"W2765700971","doi":"10.1142/s0219720017400078","title":"Reconstructing protein and gene phylogenies using reconciliation and soft-clustering","year":2017,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Université de Sherbrooke","funders":"","keywords":"Supertree; Phylogenetic tree; Ensembl; Tree (set theory); Gene; Computational biology; Cluster analysis; Biology; Heuristic; Computer science; Phylogenetics; Genetics; Genome; Genomics; Artificial intelligence; Mathematics; Combinatorics","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":[0.005508164,0.002345537,0.002814145,0.006960697,0.002778572,0.003690165,0.004866209,0.004000738,0.002582884],"category_scores_gemma":[0.01699904,0.001948441,0.004081687,0.005267607,0.002856144,0.005799972,0.004819806,0.003993619,0.001750087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002583654,"about_ca_system_score_gemma":0.002301634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003988334,"about_ca_topic_score_gemma":0.004743228,"domain_scores_codex":[0.9959072,0.001543322,0.0002314255,0.001228013,0.0007951804,0.0002948539],"domain_scores_gemma":[0.9918013,0.003715252,0.0009551303,0.002278893,0.00080589,0.0004435437],"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":[0.0006843802,0.0002699957,0.007379921,0.0006342068,0.0005370374,0.0005003978,0.0008950346,0.7345141,0.01189587,0.05430532,0.005533819,0.18285],"study_design_scores_gemma":[0.00004116052,0.00004831959,0.001001164,0.0000372882,0.00004191054,0.0001391682,0.0001609899,0.9108943,0.002936499,0.08248602,0.002153139,0.00006004902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0768524,0.0008360507,0.9164795,0.0005581325,0.00006862947,0.0001503504,0.0005944475,0.003035025,0.001425523],"genre_scores_gemma":[0.2192291,0.0004090038,0.7735013,0.0002338433,0.00006499553,0.0002647214,0.003916367,0.001263348,0.00111727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006960697,"threshold_uncertainty_score":0.02913034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02186785607877424,"score_gpt":0.2618437589356458,"score_spread":0.2399759028568716,"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."}}