{"id":"W2410820747","doi":"10.1007/978-1-62703-968-0_15","title":"Bioinformatics Identification of Coevolving Residues","year":2014,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Coevolution; Identification (biology); Inference; Biology; Computational biology; Evolutionary biology; Multiple sequence alignment; Computer science; Artificial intelligence; Sequence alignment; Genetics; Ecology; Gene; 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":[0.001497959,0.0008450557,0.001394782,0.003476806,0.001106656,0.001557162,0.001252942,0.0007186096,0.006362726],"category_scores_gemma":[0.004080316,0.0003616337,0.001406179,0.003510653,0.0003345812,0.0008217221,0.0008347466,0.001076927,0.003664574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006186777,"about_ca_system_score_gemma":0.00144234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002072783,"about_ca_topic_score_gemma":0.004594025,"domain_scores_codex":[0.9990717,0.0001696241,0.0001079841,0.0002933288,0.0002682614,0.00008904555],"domain_scores_gemma":[0.9987723,0.000469145,0.0002108235,0.0001017417,0.0003062708,0.0001396643],"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":[0.005774886,0.001643834,0.1838349,0.005282707,0.001681771,0.004502635,0.001274246,0.03786488,0.1746705,0.01937213,0.1512348,0.4128627],"study_design_scores_gemma":[0.0007831251,0.001406899,0.1191908,0.0003467936,0.00101246,0.003992241,0.001029842,0.6232663,0.05125675,0.03298967,0.1644555,0.0002695971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5064356,0.004344209,0.2886395,0.002521174,0.0004503478,0.00113706,0.1337991,0.04456749,0.01810558],"genre_scores_gemma":[0.3991435,0.001218208,0.418763,0.0005836263,0.00009885369,0.000913936,0.1735307,0.001617494,0.004130546],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006362726,"threshold_uncertainty_score":0.02128547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598108608891286,"score_gpt":0.3510013321350089,"score_spread":0.335020246046096,"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."}}