Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Gene Selection for Multiclass Prediction by Weighted Fisher Criterion | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2007 Article ID 64628 15 pages doi 2007 64628 Research Article Gene Selection for Multiclass Prediction by Weighted Fisher Criterion Jianhua Xuan 1 Yue Wang1 Yibin Dong1 Yuanjian Feng1 Bin Wang1 Javed Khan 2 Maria Zuyi Wang 1 3 Lauren Pachman 4 Sara Winokur 5 Yi-Wen Chen 3 Robert Clarke 6 and Eric Hoffman3 1 Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington VA 22203 USA 2 Department of Pediatric Oncology National Cancer Institute Gaithersburg MD 20877 USA 3 Research Center for Genetic Medicine Children s National Medical Center Washington DC 20010 USA 4 Disease Pathogenesis Program Children s Memorial Research Center Chicago IL 60614 USA 5 Department of Biological Chemistry University of California Irvine CA 92697 USA 6 Lombardi Cancer Center Georgetown University Washington DC 20007 USA Received 30 August 2006 Revised 16 December 2006 Accepted 20 March 2007 Recommended by Debashis Ghosh Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection as an important step for improved diagnostics screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step individually discriminatory genes IDGs are identified by using one-dimensional weighted Fisher criterion wFC . In the second step jointly discriminatory genes JDGs are selected by sequential search methods based on their joint class separability measured by multidimensional weighted Fisher criterion wFC . The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks ANNs and or support vector .
