{"id":"W3095905314","doi":"10.1074/mcp.ra120.002275","title":"On the Robustness of Graph-Based Clustering to Random Network Alterations","year":2020,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Michael Smith Health Research BC; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Canadian Institutes of Health Research; Genome Canada","keywords":"Cluster analysis; Robustness (evolution); Computer science; Pairwise comparison; Biological network; Graph; Network analysis; Clustering coefficient; Data mining; Computational biology; Theoretical computer science; Biology; Artificial intelligence; Genetics; Gene; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002870845,0.0002007158,0.0002033817,0.00002592496,0.0001271848,0.0000455909,0.0003586517,0.0001284355,0.00001625537],"category_scores_gemma":[0.00007235215,0.000163713,0.0001812538,0.000205809,0.00005834192,0.000002423375,0.0001472339,0.0001580441,0.000008665551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008330904,"about_ca_system_score_gemma":0.00006503719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002869103,"about_ca_topic_score_gemma":0.000004823077,"domain_scores_codex":[0.9988492,0.00009683234,0.000345157,0.0002681779,0.0001584741,0.0002821613],"domain_scores_gemma":[0.9991667,0.00002128769,0.0001313499,0.0004715636,0.00007438478,0.0001347174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002088917,0.00001789365,0.000005600296,0.00002427026,0.00004273623,0.000002236409,0.00004294068,0.6275323,0.3699698,0.0006128591,0.001335402,0.000205139],"study_design_scores_gemma":[0.000893287,0.0003574675,0.000004134155,0.00003427904,0.00002781076,0.000001448111,0.00001958077,0.2075954,0.7883743,0.0002416375,0.002214142,0.0002364884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3267449,0.0001130388,0.6706012,0.001467619,0.00008918137,0.0007474893,0.00001082442,0.000009001421,0.0002166389],"genre_scores_gemma":[0.9739542,0.000007527125,0.0208815,0.004651489,0.0002503313,0.00009591483,0.00009780426,0.00003887391,0.00002237191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6497198,"threshold_uncertainty_score":0.6676022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009868083444179611,"score_gpt":0.2002031557574816,"score_spread":0.190335072313302,"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."}}