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Báo cáo y học: "A filter-based feature selection approach for identifying potential biomarkers for lung cancer"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: A filter-based feature selection approach for identifying potential biomarkers for lung cancer | Lee et al. Journal of Clinical Bioinformatics 2011 1 11 http www.jclinbioinformatics.eom content 1 1 11 JOURNAL OF CLINICAL BIOINFORMATICS RESEARCH Open Access A filter-based feature selection approach for identifying potential biomarkers for lung cancer In-Hee Lee Gerald H Lushington and Mahesh Visvanathan Abstract Background Lung cancer is the leading cause of death from cancer in the world and its treatment is dependant on the type and stage of cancer detected in the patient. Molecular biomarkers that can characterize the cancer phenotype are thus a key tool in planning a therapeutic response. A common protocol for identifying such biomarkers is to employ genomic microarray analysis to find genes that show differential expression according to disease state or type. Data-mining techniques such as feature selection are often used to isolate from among a large manifold of genes with differential expression those specific genes whose differential expression patterns are of optimal value in phenotypic differentiation. One such technique Biomarker Identifier BMI has been developed to identify features with the ability to distinguish between two data groups of interest which is thus highly applicable for such studies. Results Microarray data with validated genes was used to evaluate the utility of BMI in identifying markers for lung cancer. This data set contains a set of 129 gene expression profiles from large-airway epithelial cells 60 samples from smokers with lung cancer and 69 from smokers without lung cancer and 7 genes from this data have been confirmed to be differentially expressed by quantitative PCR. Using this data set BMI was compared with various well-known feature selection methods and was found to be more successful than other methods in finding useful genes to classify cancerous samples. Also it is evident that genes selected by BMI given the same number of genes and classification algorithms showed better discriminative power than those from the .