Distance complexity analysis of DNA Nucleotide Sequence with Normal and Cancer Liver Cells Using Data Mining Techniques

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-27 Number-1
Year of Publication : 2015
Authors : M. Mayilvaganan, R.Rajamani
DOI :  10.14445/22312803/IJCTT-V27P102


M. Mayilvaganan, R.Rajamani "Distance complexity analysis of DNA Nucleotide Sequence with Normal and Cancer Liver Cells Using Data Mining Techniques". International Journal of Computer Trends and Technology (IJCTT) V27(1):6-9, September 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The proposed work focuses on Hidden Markov Model (HMM), has increased on the Pattern recognition domain primarily because of its strong mathematical basis and the ability to adapt to unknown of nucleotide sequence of normal and cancer affected liver cells as are pictorially represented by finite state machine. The proposed methodology will focused and analyzed about performance of DNA gene liver cancer database and normal liver cell data set from ncbi DNA data set. After analyzing the cancer cells, there is a need to determine the distance between normal and cancer affected cells. Each amino acid can have character variables and also assigned numeric number and its corresponding pair combination of sequence are represented in a graph. The proposed HMM system is validated with two different nucleotide values for analyse the performance and get the simulated output using viterbi and forward algorithms implemented in Mat Lab Tool.

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Hidden Markov Model; Viterbi algorithms; Forward algorithms; Pub Chem of liver and Cancer DNA dataset;