{"id":"W3008242172","doi":"10.1109/icmla.2019.00240","title":"Feature Clustering Towards Gene Selection","year":2019,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Feature selection; Cluster analysis; Computer science; Gene selection; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Data mining; Computational complexity theory; Feature (linguistics); Selection (genetic algorithm); Machine learning; Microarray analysis techniques; Gene; Algorithm; Biology; Gene expression","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":[0.00211943,0.001263535,0.001624987,0.002944968,0.000600446,0.001066571,0.001119229,0.0009938073,0.001453911],"category_scores_gemma":[0.005587143,0.0004014481,0.001336167,0.003536143,0.0006084032,0.0006883607,0.0008816969,0.00103384,0.001392181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006416093,"about_ca_system_score_gemma":0.000811927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001594241,"about_ca_topic_score_gemma":0.001135632,"domain_scores_codex":[0.9982812,0.0006774132,0.00007408259,0.0003617611,0.0004735562,0.0001319378],"domain_scores_gemma":[0.9973432,0.001356661,0.0001661637,0.0003512349,0.0007233478,0.00005933751],"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":[0.0005870382,0.0002398837,0.0046377,0.0003258006,0.0002878841,0.0002431214,0.0001552939,0.1079273,0.04315533,0.009205589,0.01132983,0.8219051],"study_design_scores_gemma":[0.000108802,0.0003296743,0.00621096,0.00006256436,0.0001729907,0.00043197,0.00008673139,0.8902932,0.04842282,0.03537817,0.01842922,0.00007286471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02094843,0.0008783645,0.9745341,0.0002807108,0.00009682834,0.0001449901,0.0003127777,0.00160092,0.001202859],"genre_scores_gemma":[0.2792128,0.0009625042,0.7124416,0.000424911,0.0003101325,0.000636266,0.002366745,0.0003907699,0.00325432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002944968,"threshold_uncertainty_score":0.01120877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007970929783853602,"score_gpt":0.24618430914696,"score_spread":0.2382133793631064,"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."}}