Estimation of Distance complexity in amino acids between Normal and Cancer Liver Cells using Data Mining Techniques
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2015 by IJCTT Journal | ||
| Volume-27 Number-1 |
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| Year of Publication : 2015 | ||
| Authors : M. Mayilvaganan, R.Rajamani | ||
| DOI : 10.14445/22312803/IJCTT-V27P103 |
M. Mayilvaganan, R.Rajamani "Estimation of Distance complexity in amino acids between Normal and Cancer Liver Cells using Data Mining Techniques". International Journal of Computer Trends and Technology (IJCTT) V27(1):10-13, September 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
The Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Clustering algorithm used to find groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups . This paper comprises of two database such as normal liver cells and cancer affected cells. 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. The extracted rules and analyzed results are graphically demonstrated. The performance is analyzed based on the different no of instances and confidence in DNA sequence data set.
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Keywords
Hidden Markov Model; Viterbi algorithms; Forward algorithms; Pub Chem of liver and Cancer DNA dataset;