An Approach to Identify Gene Markers Relating to Viral Carcinogenesis using Data Mining Tools
Authors
-
Department of Computer Science and Engineering, University of Kalyani, Kalyani, Nadia, West Bengal ,IN
Paramita Biswas -
Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal ,INBandana Barman
DOI:
https://doi.org/10.24906/isc/2019/v33/i2/183890Keywords:
Microarray Data, Fuzzy C-Means Clustering, Euclidean Distance, Hepatitis C, Cluster Evaluation, Neural Network, Viral Carcinogenesis, HBV, HCV, HPV, HTLV-I, EBV, KSHV.Abstract
Genes are the smallest hereditary unit in any living organism. Several genes work together and regulate each other to accomplish different cellular processes. In order to perform specific cellular task genetic information has been used for protein synthesis. According to recent studies, these entire procedures react through the change in the expression level of genes. Therefore, monitoring the changes in expression patterns over time provides a distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Nowadays, with the help of microarray technology, it is possible to measure the mRNA expression level of thousands of genes simultaneously. Therefore, Hepatitis C, virus (HCV) infected microarray data set has been collected to carry out the proposed experiments. In this paper, gene expression changes due to Hepatitis C virus (HCV) infection have been studied. Then the genes having almost similar expression pattern in all the time points were grouped together. Next, biological significance analysis of those groups of genes is performed to identify the gene markers not only related to viral infection processes but also actively taking part in viral carcinogenesis process.
Index Terms-Microarray data, fuzzy c-means clustering, Euclidean distance, hepatitis C, cluster evaluation, neural network, viral carcinogenesis, HBV, HCV, HPV, HTLV-I, EBV, and KSHV. An Approach to Identify Gene Markers Relating to Viral Carcinogenesis Using Data Mining Tools Paramita Biswas1 and Bandana Barman2 S
Downloads
Downloads
Published
How to Cite
Issue
Section
References
C. M. O’Connor and J. U. Adams, Essentials of Cell Biology. Cambridge, MA: NPG Education, 2010.
Z. Herceg and P. Hainaut, “Genetic and epigenetic alterations as biomarkers for cancer detection, diagnosis and prognosis,†Molecular oncology, vol. 1, no. 1, pp. 26–41, 2007.
Y. Chen, V. Williams, M. Filippova, V. Filippov, and P. Duerksen-Hughes, “Viral carcinogenesis: factors inducing dna damage and virus integration,†Cancers, vol. 6, no. 4, pp. 2155–2186, 2014.
A. J. Levine, “The common mechanisms of transformation by the small dna tumor viruses: The inactivation of tumor suppressor gene products: p53,†Virology, vol. 384, no. 2, pp. 285–293, 2009.
I. Androulakis, E. Yang, and R. Almon, “Analysis of timeseries gene expression data: methods, challenges, and opportunities,†Annu. Rev. Biomed. Eng., vol. 9, pp. 205– 228, 2007.
C. M. Bishop, Neural networks for pattern recognition. 1995.
P. T. Pearson, “Visualizing clusters in artificial neural networks using morse theory,†Advances in Artificial Neural Systems, vol. 2013, p. 8, 2013.
C.-C. Chang and S.-H. Chen, “A comparative analysis on artificial neural network-based two-stage clustering,†Cogent Engineering, vol. 2, no. 1, p. 995785, 2015.
T. S. Madhulatha, “An overview on clustering methods,†IOSR Journal of Engineering, vol. 2, no. 4, pp. 719–725, 2012.
T. Calin´ski and J. Harabasz, “A dendrite method for cluster analysis,†Communications in Statistics-theory and Methods, vol. 3, no. 1, pp. 1–27, 1974.
