Analyzing Prognosis Characteristics of Hepatitis C using a Biclustering Based Approach

DNA microarray technology has led to huge advancement in the diagnosis process of diseases. In this paper, we have developed a framework to analyze gene expression data of three different Hepatitis C related prognosis datasets. First, we have discovered differentially expressed genes for each pair of prognosis datasets. Principal Component Analysis has been carried out for reduction of samples size and computational convenience. The difference in local expression patterns between each pair of prognosis is identified through similarity scores among biclusters. A gene ontology and pathway based analysis has been performed for understanding biological significance of the identified biclusters.