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An Approach to Identify Gene Markers Relating to Viral Carcinogenesis using Data Mining Tools


Affiliations
1 Department of Computer Science and Engineering, University of Kalyani, Kalyani, Nadia, West Bengal, India
2 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, India
     

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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


Keywords

Microarray Data, Fuzzy C-Means Clustering, Euclidean Distance, Hepatitis C, Cluster Evaluation, Neural Network, Viral Carcinogenesis, HBV, HCV, HPV, HTLV-I, EBV, KSHV.
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  • An Approach to Identify Gene Markers Relating to Viral Carcinogenesis using Data Mining Tools

Abstract Views: 469  |  PDF Views: 3

Authors

Paramita Biswas
Department of Computer Science and Engineering, University of Kalyani, Kalyani, Nadia, West Bengal, India
Bandana Barman
Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, India

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


Keywords


Microarray Data, Fuzzy C-Means Clustering, Euclidean Distance, Hepatitis C, Cluster Evaluation, Neural Network, Viral Carcinogenesis, HBV, HCV, HPV, HTLV-I, EBV, KSHV.

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DOI: https://doi.org/10.24906/isc%2F2019%2Fv33%2Fi2%2F183890