{"id":"W2118446779","doi":"10.1093/bioinformatics/btg415","title":"Functional topology in a network of protein interactions","year":2004,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":430,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computational biology; Computer science; Network analysis; Function (biology); Interaction network; Construct (python library); Mutation; Protein–protein interaction; Network model; Set (abstract data type); Biological network; Biology; Genetics; Artificial intelligence; Gene; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.0001510532,0.0001004056,0.0001283544,0.00004088399,0.00003706217,0.000009035948,0.0001072557,0.0001098096,0.00002785158],"category_scores_gemma":[0.00002651782,0.0000946753,0.00006208968,0.0001235041,0.00007157816,0.000009281924,0.0000817787,0.0001016985,0.00002287419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002662345,"about_ca_system_score_gemma":0.0001164899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001756509,"about_ca_topic_score_gemma":0.000110399,"domain_scores_codex":[0.9991954,0.000009415361,0.0004386938,0.00007237679,0.00007542289,0.0002086901],"domain_scores_gemma":[0.9995437,0.00000603755,0.0001522865,0.0002042993,0.0000514547,0.00004219421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001716601,0.001535763,0.01403775,0.00119386,0.0007824909,0.00001383526,0.004917903,0.4570121,0.100128,0.2530143,0.03974316,0.1259043],"study_design_scores_gemma":[0.02962434,0.005891891,0.06539228,0.001733005,0.0001993402,0.0008414533,0.005807293,0.05057667,0.1668124,0.1810956,0.4870909,0.00493473],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7743024,0.0004282345,0.2025331,0.0005918019,0.0007656839,0.000732676,0.00003117848,0.00002366503,0.02059132],"genre_scores_gemma":[0.9796134,0.00002855062,0.01955693,0.0002461252,0.0001779139,0.00002107026,0.0001025449,0.000008212296,0.0002452565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4473478,"threshold_uncertainty_score":0.3860747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075372265891568,"score_gpt":0.2299940728744953,"score_spread":0.2192403502155796,"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."}}