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Microarrays have now gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to novel algorithms for analyzing changes in expression profiles. In a micro-RNA (miRNA) or gene-expression profiling experiment, the expression levels of thousands of genes/miRNAs...
Statistical analysis and association rule mining are two most efficient techniques, where the first one is used to identify differentially expressed/methylated genes across different types of samples or experimental conditions and the second one is used to determine expression/methylation relationships among them. In this article, we have performed an integrated analysis of statistical methods and...
With the advancement of microarray technology, gene expression profiling has shown great potential in outcome prediction for different types of cancers. Microarray cancer data, organized as samples versus genes fashion, are being exploited for the classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer...
Clustering methods are used for unsupervised classification of tumor subclasses in microarray gene expression data sets organized in a fashion where the rows represent the tumor samples and columns represent the genes. Clustering algorithms can be very sensitive with respect to the set of features (genes) considered in the clustering process. It is important to select the set of informative and relevant...
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