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Analysis of Liver Cancer DNA Sequence Data using Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 61 - Number 3 |
| Year of Publication: 2013 |
| Authors: N.senthil Vel Murugan, V.vallinayagam, K. Senthamarai Kannan, T. Viveka |
10.5120/9909-4502
|
N.senthil Vel Murugan, V.vallinayagam, K. Senthamarai Kannan, T. Viveka . Analysis of Liver Cancer DNA Sequence Data using Data Mining. International Journal of Computer Applications. 61, 3 ( January 2013), 23-25. DOI=10.5120/9909-4502
Abstract
Data mining is flourishing during recent years and it is establishing itself as a major discipline in Computer Science and Statistics with industrial relevance. Data mining is finding interesting structure in databases [2]. DNA is an extraordinary chip data with thousands of attributes which represents the gene expression values [4]. The DNA data set are stored in huge biological databases for several purposes [1]. Cancer occurs when lumps of cells usually group together to form tumors. A growing tumor becomes a lump of cancer cells that can destroy the normal cells around the tumor and damage the body's health tissues. In the last two decades the researchers have drawn much attention about liver cancer. Liver cancer is a disease in which malignant cells form in the tissues of the liver. It is relatively rare form of cancer but has a high mortality rate. The aim of this paper is to analyze the liver cancer DNA sequence data using the generalization of Kimura Models and Markov Chain. The reasonable results verify the validity of our method.
References
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- Fayad, U. M et al. , 1996 Advances in knowledge discovery and data mining, AAAI/MIT press.
- Kimura. M, 1980 A simple method for estimating evolutionary rate in a finite population due to mutational production of neutral and nearly neutral base substitution through comparative studies of nucleotide sequences, J. Molec. Biol. Pg. 111-120.
- Langdon et al. , 2004, Genetic programming for mining DNA chip data from cancer patients, University College, London
- Tan Wai-Yuan,(2002) "Stochastic Models with Applications to Genetics, Cancers, Aids and other Biomedical systems", World Scientific Publishing Co. Ltd.
- Warren . J. Ewens et. al (2004), "Statistical methods in Bioinformatics", Springer Publication, New Delhi
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