Demonstrating Protein Analysis for cancer Disease using Approximation Algorithms
A. Rajapriya1, A. Nagarajan2
1A. Raja priya, Research Scholar, Department of Computer Applications, Alagappa University- Karaikudi. Tamil Nadu India.
2Dr. A. Nagarajan, Assistant Professor, 2Department of ComputerApplications, Alagappa University- Karaikudi. Tamil Nadu India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 985-987 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6531068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cancer is the most hazardous disease. If it went on last stage then it is very difficult to cure but, if it detect at earlier stage then it may be curable. Cancer detection by using sequence alignment technique is one of the important research topics of bioinformatics. It searches for similarities and identification of organic mutations between protein sequence and DNA sequence. There are many heuristic and non-heuristic algorithm used for sequence alignment. The research focuses on developing a system to check out the different approximation algorithms performance for detection of cancer. This research work demonstrates the use of Smith Waterman Algorithm for similarity matching of protein sequences and hybrid algorithm for cancer disease detection at early stage.
Keyword: Protein Analysis, Cancer Disease, Early Prediction, Approximation algorithms.
Scope of the Article: Analysis of Algorithms and Computational Complexity.