Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2011.021227
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 2 Issue 12, 2011.
Abstract: Summary: Several papers have been published about the prediction of hepatitis C virus (HCV) polyprotein cleavage sites, using symbolic and non-symbolic machine learning techniques. The published papers achieved different Levels of prediction accuracy. the achieved results depends on the used technique and the availability of adequate and accurate HCV polyprotein sequences with known cleavage sites. We tried here to achieve more accurate prediction results, and more Informative knowledge about the HCV protein cleavage sites using Decision tree algorithm. There are several factors that can affect the overall prediction accuracy. One of the most important factors is the availably of acceptable and accurate HCV polyproteins sequences with known cleavage sites. We collected latest accurate data sets to build the prediction model. Also we collected another dataset for the model testing. Motivation: Hepatitis C virus is a global health problem affecting a significant portion of the world’s population. The World Health Organization estimated that in1999; 170 million hepatitis C virus (HCV) carriers were present worldwide, with 3 to 4 million new cases per year. Several approaches have been performed to analyze HCV life cycle to find out the important factors of the viral replication process. HCV polyprotein processing by the viral protease has a vital role in the virus replication. The prediction of HCV protease cleavage sites can help the biologists in the design of suitable viral inhibitors. Results: The ease to use and to understand of the decision tree enabled us to create simple prediction model. We used here the latest accurate viral datasets. Decision tree achieved here acceptable prediction accuracy results. Also it generated informative knowledge about the cleavage process itself. These results can help the researchers in the development of effective viral inhibitors. Using decision tree to predict HCV protein cleavage sites achieved high prediction accuracy.
Ahmed mohamed samir ali gamal eldin, “A Data Mining Approach for the Prediction of Hepatitis C Virus protease Cleavage Sites” International Journal of Advanced Computer Science and Applications(IJACSA), 2(12), 2011. http://dx.doi.org/10.14569/IJACSA.2011.021227