A Study on Nucleotide Cancer Liver Cells with DNA Binding Using Hidden Markov Model

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-23 Number-4
Year of Publication : 2015
Authors : M. Mayilvaganan, R.Rajamani
  10.14445/22312803/IJCTT-V23P135

MLA

M. Mayilvaganan, R.Rajamani "A Study on Nucleotide Cancer Liver Cells with DNA Binding Using Hidden Markov Model". International Journal of Computer Trends and Technology (IJCTT) V23(4):170-174, May 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The work will be focused and analyzed about performance of DNA gene liver cancer database and normal liver cell data set from ncbi DNA data set. 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 nucleotide sequence of biological databases is growing long terms of quantity, memory and complexity, managing these databases is becoming very complex. In this paper focuses 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. It is a finite automaton with a fixed number of states which are trained to maximize the probability of the observation sequence by using viterbi algorithm and forward algorithm. 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.

References
[1] M.Anandavalli , M.K.Ghose , K.Gouthaman ,Association Rule Mining in Genomics,International journal of computer Theory and engineering, Vol.2,No.2 April,2010.
[2] Bayardo, Roberto J., Jr.; Agrawal, Rakesh; Gunopulos, Dimitrios (2000). Constraint-based rule mining in large, dense databases Data Mining and Knowledge Discovery (2): 217–240.
[3] Donald, ―Introduction to Data Mining for Medical Informatics, Clin Lab Med, pp. 9-35, 2008.
[4] JunoWatada,KeisukeAoki, Masahiro Kawano, Muhammad SuzuriHitam, Dual Scaling Approach to Data M Journal of Advanced Computational Intelligence Intelligent Informatics , Vol. 10, No. 4, pp. 441-447, 2006.
[5] Jiawei Han and MichelineKamber,―Data Mining Concepts and Techniques. San Francisco, CA: Elsevier Inc, 2006.
[6] Irene M. Mullins et al., ―Data mining and clinical data repositories: Insights from a667,000 patient data set, Computers in Biology and Medicine, vol. 36, pp. 1351-1377, 2006.
[7] Liao.S & M. Embrechts I. -N. Lee, ―Data mining techniques applied to medical information, Med. Inform , pp. 81-102, 2000.
[8] Piatetsky-Shapiro, G.& myth P. &Uthurusamy, R. Fayyad, "From Data Mining toKnowledge Discovery: An Overview," in Advances in Knowledge Discovery and DataMining, 1996.
[9] Webb, Geoffrey I. ―Efficient Search for Association Rules, Proceedings of the Sixth ACM SIGKDD International Conference Knowledge Discoveryand Data Mining (KDD-2000), Boston, MA, New York.
[10] R. Zhang, Y, Katta, ―Medical Data Mining,Data Mining and Knowledge Discovery, pp. 305-308, 2002.

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