{"id":"W2771947453","doi":"10.1142/s0129626417500074","title":"A Parallel Local Search Algorithm for Clustering Large Biological Networks","year":2017,"lang":"en","type":"article","venue":"Parallel Processing Letters","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Algoma University","funders":"","keywords":"Cluster analysis; Computer science; Set (abstract data type); Algorithm; Cluster (spacecraft); Canopy clustering algorithm; Quality (philosophy); CURE data clustering algorithm; Correlation clustering; Data stream clustering; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009627361,0.0008606534,0.001095373,0.001354177,0.001152739,0.000988806,0.001789916,0.001133636,0.004187588],"category_scores_gemma":[0.002378581,0.0005770257,0.0008586453,0.002096899,0.0008241287,0.001004726,0.001356917,0.001332912,0.002413668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171319,"about_ca_system_score_gemma":0.001494296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005132589,"about_ca_topic_score_gemma":0.00737373,"domain_scores_codex":[0.9993857,0.0001468831,0.00003112845,0.0001467961,0.0002446244,0.00004486256],"domain_scores_gemma":[0.9993883,0.0002033807,0.00007163519,0.0001203948,0.0001711295,0.00004515899],"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":[0.0003238995,0.00011751,0.0009275171,0.0002805211,0.0001769945,0.0001960819,0.0002722724,0.5708854,0.01917767,0.01982387,0.01817616,0.3696422],"study_design_scores_gemma":[0.00005654163,0.00003851192,0.000138554,0.00001000534,0.00001579745,0.00007170239,0.00002106449,0.9817048,0.003226751,0.01046663,0.004235471,0.000014152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003763298,0.000229205,0.9922377,0.0001203376,0.00003099956,0.0000712568,0.00008238264,0.002396794,0.001067945],"genre_scores_gemma":[0.06142708,0.0001813068,0.9324805,0.0001537561,0.00003948547,0.0004606108,0.000529387,0.0005073181,0.004220468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005132589,"threshold_uncertainty_score":0.01400888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02437830927219367,"score_gpt":0.2814118185182725,"score_spread":0.2570335092460788,"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."}}