{"id":"W2408920437","doi":"10.1038/srep16971","title":"Combined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL): A Robust Method for Selection of Cluster Number, K","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"U.S. National Library of Medicine; National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute","keywords":"Cluster analysis; Computer science; Set (abstract data type); Data mining; sort; Single-linkage clustering; CURE data clustering algorithm; Affinity propagation; Stability (learning theory); Correlation clustering; Consensus clustering; Cluster (spacecraft); Data set; Determining the number of clusters in a data set; Complete-linkage clustering; Clustering high-dimensional data; Pattern recognition (psychology); Artificial intelligence; Machine learning; Information retrieval","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":[0.01432969,0.002215791,0.002421782,0.00577082,0.003022198,0.003414008,0.003689033,0.002487632,0.003413186],"category_scores_gemma":[0.05737389,0.001278781,0.003336566,0.004666657,0.002371991,0.003190044,0.006192893,0.003301208,0.001862576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001448223,"about_ca_system_score_gemma":0.002496936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003283367,"about_ca_topic_score_gemma":0.004124887,"domain_scores_codex":[0.9879867,0.005820852,0.0007284431,0.002572077,0.002517585,0.0003742535],"domain_scores_gemma":[0.9750568,0.01037437,0.002682629,0.006370289,0.005147003,0.0003688407],"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":[0.001115196,0.0002642607,0.0117136,0.001006188,0.001589853,0.0002752563,0.001603146,0.1510656,0.01497697,0.04420574,0.02208826,0.750096],"study_design_scores_gemma":[0.0001781095,0.0002485289,0.003783342,0.0001636047,0.0002421231,0.0005161366,0.0003632585,0.9029527,0.01964461,0.05177948,0.01988767,0.0002404631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004627092,0.0001930117,0.992452,0.0001239164,0.00005418959,0.0001250936,0.0001419952,0.001693759,0.0005890458],"genre_scores_gemma":[0.07991519,0.0001214197,0.9165091,0.0001264453,0.00006838042,0.0005519676,0.00051718,0.001161762,0.001028512],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01432969,"threshold_uncertainty_score":0.07578355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.048644546604193,"score_gpt":0.3125900545353345,"score_spread":0.2639455079311415,"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."}}