Identification of differentially expressed genes by unsupervised learning method.

Venkatesan, P.; Fathima, J.

International Journal of Data Mining Techniques and Applications; 2013; 2; 121-125.

Abstract: Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring of gene expression of tens of thousands of genes in parallel. Micro array analysis include many stages. Extracting samples from the cells, getting the gene expression matrix from the raw data, and data normalization which are low level analysis. Cluster analysis for genome-wide expression data from DNA micro array data is described as a high level analysis that uses standard statistical algorithms to arrange genes according to similarity patterns of expression levels. This paper presents a method for the number of clusters using the divisisive hierarchical clustering, and k-means clustering of significant genes. The goal of this method is to identify genes that are strongly associated with disease in 12607 genes. Gene filtering is applied to identify the clusters.

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