{"id":"W58699125","doi":"10.1007/978-3-642-23038-7_27","title":"Clustering with Overlap for Genetic Interaction Networks via Local Search Optimization","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Computer science; Disjoint sets; Modular design; Biological network; Genetic algorithm; Artificial intelligence; Data mining; Computational biology; Machine learning; Biology; Mathematics","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":[0.002393014,0.0016381,0.002948292,0.002271578,0.001293365,0.001201458,0.003924456,0.002380008,0.005412627],"category_scores_gemma":[0.006379728,0.001269626,0.00199414,0.002243456,0.00171489,0.002193753,0.003513334,0.001826219,0.001262462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00187366,"about_ca_system_score_gemma":0.001474699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006039525,"about_ca_topic_score_gemma":0.006300907,"domain_scores_codex":[0.9989408,0.0004810405,0.00004535573,0.0002183641,0.0002207124,0.00009377361],"domain_scores_gemma":[0.9967151,0.002467155,0.0001686107,0.0002501174,0.0002725401,0.0001264849],"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":[0.0001197527,0.00008660386,0.0003020691,0.0001173318,0.00008827914,0.00005034351,0.00009083974,0.9175746,0.0008754921,0.02507085,0.002224405,0.05339939],"study_design_scores_gemma":[0.00000862069,0.000009579747,0.00002682147,0.000003855724,0.000006222903,0.000007227848,0.000007458964,0.9907449,0.0001447893,0.00882864,0.0002088755,0.000002973965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007583175,0.0001817777,0.9898413,0.00009792182,0.00002087864,0.00006970063,0.00007015505,0.0006250749,0.001509971],"genre_scores_gemma":[0.2279568,0.0002391795,0.763221,0.0001606959,0.00007819679,0.0009880896,0.0006469756,0.0007967127,0.005912366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006039525,"threshold_uncertainty_score":0.01810706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134177175960366,"score_gpt":0.2267533661801837,"score_spread":0.2154115944205801,"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."}}