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This study developed a method to identify disease-correlated pathways by integrating copy numbers (CN) and gene expression (GE). To evaluate the correlation between CN and GE, a suitable window size was assessed by simulation. Gene Set Enrichment Analysis (GSEA) was utilized to identify the possible pathways by CN, GE, and their correlations, respectively. Each of those enriched pathways was further...
The role of micro array expression data in cancer diagnosis is very significant. Mining for useful information from such micro array data consisting of thousands of genes and a small number of samples is often a tough task. Colon cancer is the second most common cause of cancer mortality in Western countries. According to the WHO 2006 report colorectal cancer causes 655,000 deaths worldwide per year...
Recently there is an increasing interest in changing the criterion of tumor classification from morphologic to molecular. In this perspective, the problem can be regarded as a classification problem in machine learning. In this study wavelet analysis is used to extract the features from high dimensional microarray profiles. To make it easier to find the significant genes, we remove the small change...
Recently, gene expression profiling using DNA microarray technique has been shown as a promising tool to improve the diagnosis and treatment of cancer. Support vector machine has been successfully used to classify cancer tissue based on gene expression data. Besides performance, the ability to discover underlying principles will be a crucial point in the medical field. In this paper, we present a...
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