Identification of Genetic Pathway for Cervical Cancer Development Using Rough and Bayesian Theory

Suitable analysis of microarray dataset can unlock the mystery of the origin of many dreaded disease like cancer which can subsequently be investigated for its rectification, resulting into search for drug design. A critical challenge of the post-genomic era is to find out the cancer causing genes that induce changes in gene expression profiles in the microarray dataset. Various algorithms based on SVM, Data Mining Techniques, Information theory based investigations, Clustering Techniques etc. were used by previous researchers. In this paper, Rough Set Theory and Bayesian Network based techniques have been applied for the same purpose. Rough Set has been used to isolate genes from microarray dataset responsible for cervical cancer. Bayesian approach has been used for extracting the Gene Regulating Network using the isolated genes. The same has been repeated for a normal healthy person. By superimposing these two networks, it is possible to find out the distinct cellular pathway for development of cancer from the departure of directed edges of the two networks. The results obtained in this work are quite satisfactory.

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