{"id":"W2004805762","doi":"10.1002/prot.20948","title":"Codep: Maximizing co‐evolutionary interdependencies to discover interacting proteins","year":2006,"lang":"en","type":"article","venue":"Proteins Structure Function and Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Ontario Institute for Cancer Research","funders":"","keywords":"Phylogenetic tree; Interdependence; Similarity (geometry); Computational biology; Computer science; Evolutionary algorithm; Property (philosophy); Sequence (biology); Evolutionary biology; Order (exchange); Biology; Genetics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001100552,0.0002991977,0.000199981,0.0001323918,0.0002814323,0.0001594218,0.0001731475,0.0002212671,0.00004403698],"category_scores_gemma":[0.00008883603,0.0002471775,0.00007774385,0.0001510632,0.00008359214,0.00005295499,0.0002229759,0.0002278566,0.00001104148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004263503,"about_ca_system_score_gemma":0.0000811931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007168884,"about_ca_topic_score_gemma":0.0001928554,"domain_scores_codex":[0.9986317,0.00002957798,0.0004337678,0.0002972313,0.0002693567,0.0003383366],"domain_scores_gemma":[0.9992676,0.000009723418,0.000192714,0.0003047262,0.000114793,0.0001103763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001378826,0.0001068551,0.01295744,0.0006616578,0.0002254817,0.000007161065,0.0006603851,0.001921596,0.9233297,0.005044428,0.008721492,0.04498496],"study_design_scores_gemma":[0.007107804,0.005574957,0.07598495,0.0007286055,0.000321276,0.001486902,0.004404899,0.02895138,0.503454,0.02242848,0.3444433,0.005113506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6150408,0.0003224058,0.3770791,0.000393018,0.0006374972,0.001838361,0.0002722558,0.0001102691,0.00430623],"genre_scores_gemma":[0.9433661,0.00001175207,0.05453276,0.0005659811,0.0004375837,0.00005480189,0.0004841853,0.0000269138,0.0005199898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4198758,"threshold_uncertainty_score":0.999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0048395997745828,"score_gpt":0.2168727243904019,"score_spread":0.2120331246158191,"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."}}