{"id":"W2407408969","doi":"10.1007/978-1-61779-276-2_11","title":"Using Coevolution to Predict Protein–Protein Interactions","year":2011,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Health and Long-Term Care; Universities Space Research Association","keywords":"Coevolution; Benchmark (surveying); Immunoprecipitation; Computational biology; Computer science; Protein–protein interaction; Obligate; Scope (computer science); Focus (optics); Biology; Artificial intelligence; Evolutionary biology; Machine learning; Genetics; Gene; Physics; Ecology; Geography","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":[0.003169088,0.0006414925,0.0008831439,0.00283002,0.0004734927,0.0009102265,0.0007848507,0.0009058115,0.000782581],"category_scores_gemma":[0.01101211,0.0004002318,0.0009354735,0.001463999,0.0005917639,0.001385422,0.001122817,0.0009677915,0.0003753024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000730518,"about_ca_system_score_gemma":0.0004274668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00174524,"about_ca_topic_score_gemma":0.001625282,"domain_scores_codex":[0.9987577,0.0006508681,0.00009700352,0.000227271,0.0002119425,0.0000551882],"domain_scores_gemma":[0.9956549,0.003108816,0.0005273602,0.0003783004,0.0002212726,0.0001093242],"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":[0.0005034283,0.0003091976,0.1286011,0.0002384833,0.001293188,0.0003771221,0.0002861593,0.6636177,0.02618415,0.01035888,0.001087586,0.1671431],"study_design_scores_gemma":[0.00000568972,0.00003372754,0.004564426,0.000006002154,0.00002072322,0.0001003731,0.00001319154,0.9887028,0.002142348,0.004161908,0.0002367916,0.00001211117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.571986,0.0009549049,0.4232413,0.0002986216,0.00002523796,0.0000872265,0.0002408438,0.0009245994,0.002241299],"genre_scores_gemma":[0.9159371,0.0002434807,0.08278558,0.00007421544,0.00001216735,0.00008025077,0.0003355407,0.00008015073,0.0004515198],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003169088,"threshold_uncertainty_score":0.01675993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04519276612433822,"score_gpt":0.3743894862750629,"score_spread":0.3291967201507247,"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."}}