{"id":"W2765676440","doi":"10.1093/molbev/msx283","title":"CompositeSearch: A Generalized Network Approach for Composite Gene Families Detection","year":2017,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"FP7 Ideas: European Research Council","keywords":"Shuffling; Biology; Computational biology; Gene; Genetics; Similarity (geometry); Component (thermodynamics); Homology (biology); Artificial intelligence; Computer science","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":[0.001638129,0.002393201,0.001544556,0.008118716,0.001449383,0.002427246,0.002455576,0.001420774,0.006640065],"category_scores_gemma":[0.007700714,0.001021045,0.002217686,0.004161975,0.0007414345,0.002706434,0.002531905,0.001541116,0.002338501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008148576,"about_ca_system_score_gemma":0.001659961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006120855,"about_ca_topic_score_gemma":0.01070961,"domain_scores_codex":[0.9985599,0.0003289481,0.00009303881,0.0005042757,0.0004164318,0.00009744518],"domain_scores_gemma":[0.9974453,0.001377187,0.0003054089,0.000386294,0.0003475856,0.0001382178],"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":[0.0009912334,0.0003881821,0.01937736,0.002755256,0.001907078,0.001546308,0.001329465,0.1357424,0.05139498,0.03705666,0.05944182,0.6880692],"study_design_scores_gemma":[0.00005846074,0.00009102151,0.003151943,0.00008877683,0.0001728384,0.0008921206,0.0002708009,0.9015468,0.009452215,0.05614925,0.028034,0.00009176217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01611689,0.0005062311,0.9586295,0.0001963761,0.00005840032,0.0002601102,0.005517354,0.01677138,0.001943862],"genre_scores_gemma":[0.06649564,0.0004244775,0.9160128,0.0001061814,0.00005730386,0.0005940522,0.01200157,0.001859443,0.002448585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008118716,"threshold_uncertainty_score":0.02221328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670598537862296,"score_gpt":0.2745868233035209,"score_spread":0.257880837924898,"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."}}