{"id":"W2343938190","doi":"10.1109/tcbb.2016.2515582","title":"A New Approach for Feature Selection from Microarray Data Based on Mutual Information","year":2016,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Feature selection; Mutual information; Computer science; Data mining; Relevance (law); Feature (linguistics); Heuristic; Artificial intelligence; Interaction information; Boosting (machine learning); Machine learning; Pattern recognition (psychology); 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":[0.002708016,0.001353036,0.002057205,0.003706933,0.0006207582,0.0009697404,0.001667859,0.0009808461,0.0008912724],"category_scores_gemma":[0.004679283,0.000469868,0.002029927,0.00310077,0.0006296725,0.001220899,0.001215892,0.00141164,0.0005131508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000677985,"about_ca_system_score_gemma":0.0007866372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001154514,"about_ca_topic_score_gemma":0.001304105,"domain_scores_codex":[0.9973997,0.000617088,0.0001855849,0.0006066088,0.00104109,0.0001499223],"domain_scores_gemma":[0.9983616,0.000846077,0.0001748995,0.0001635692,0.000396742,0.00005693865],"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":[0.000293056,0.000215504,0.003945379,0.0003397839,0.0005500028,0.0003084574,0.0001859386,0.08680371,0.05354678,0.01260379,0.006782738,0.8344249],"study_design_scores_gemma":[0.00005549443,0.0003401564,0.004173454,0.00002921853,0.0001732932,0.0004628562,0.00003924961,0.9515957,0.01414818,0.01996392,0.008935058,0.00008343517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002911543,0.0002787914,0.9960685,0.00008747763,0.00002514984,0.00005301988,0.00006819364,0.00029988,0.0002074029],"genre_scores_gemma":[0.1437971,0.0005134797,0.8521064,0.0003360267,0.0003359855,0.000677874,0.0008236759,0.0001596019,0.001249862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003706933,"threshold_uncertainty_score":0.01432157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02435361130096077,"score_gpt":0.2798957858204131,"score_spread":0.2555421745194523,"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."}}