{"id":"W17499823","doi":"10.1007/978-3-642-39159-0_1","title":"A Fast Agglomerative Community Detection Method for Protein Complex Discovery in Protein Interaction Networks","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hierarchical clustering; Vertex (graph theory); Protein Interaction Networks; Computer science; Cluster analysis; Hierarchical clustering of networks; Data mining; Theoretical computer science; Algorithm; Pattern recognition (psychology); Protein–protein interaction; Artificial intelligence; Biology; Graph; Canopy clustering algorithm; Fuzzy clustering","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":[0.002508834,0.001846313,0.002408838,0.005355482,0.002382451,0.001531307,0.004637045,0.001910895,0.003787231],"category_scores_gemma":[0.005250726,0.001283321,0.002704594,0.004303742,0.0009490534,0.001991733,0.00283496,0.002075747,0.00268446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009254475,"about_ca_system_score_gemma":0.002050483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01195866,"about_ca_topic_score_gemma":0.02068189,"domain_scores_codex":[0.9980743,0.0003732023,0.0001222031,0.0003731161,0.0008966901,0.0001605521],"domain_scores_gemma":[0.9969721,0.001261043,0.0001545611,0.0004785661,0.0009487857,0.0001850035],"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":[0.0006339454,0.0004625662,0.003308716,0.0005573187,0.0008677849,0.0003926383,0.0003990425,0.09650388,0.03099035,0.009707032,0.02712884,0.8290479],"study_design_scores_gemma":[0.00005962442,0.00004513948,0.001106804,0.0000183471,0.00008500142,0.0002399223,0.00005301234,0.9791213,0.00641532,0.008861981,0.003942935,0.00005063261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008783998,0.0003937268,0.9855402,0.0001060792,0.00008974265,0.0001741312,0.0004083785,0.004059673,0.0004440381],"genre_scores_gemma":[0.03280436,0.0001748825,0.9628092,0.00006111086,0.00006155646,0.0002568443,0.001224456,0.0004410592,0.002166563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01195866,"threshold_uncertainty_score":0.02377808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878246329695563,"score_gpt":0.2704596346008749,"score_spread":0.2516771713039192,"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."}}