A new approach for HIV-1 protease cleavage site prediction combined with feature selection

Yao Yuan, Hui Liu, Guangtao Qiu

Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Tech- nology, Dalian, China.
The Second Department, PLA Communication and Command Academy, Wuhan, China.

DOI: 10.4236/jbise.2013.612144                

Abstract

Acquired immunodeficiency syndrome (AIDS) is a fatal disease which highly threatens the health of human being. Human immunodeficiency virus (HIV) is the pathogeny for this disease. Investigating HIV-1 protease cleavage sites can help researchers find or develop protease inhibitors which can restrain the replication of HIV-1, thus resisting AIDS. Feature selection is a new approach for solving the HIV-1 protease cleavage site prediction task and it’s a key point in our research. Comparing with the previous work, there are several advantages in our work. First, a filter method is used to eliminate the redundant features. Second, besides traditional orthogonal encoding (OE), two kinds of newly proposed features extracted by conducting principal component analysis (PCA) and non-linear Fisher transformation (NLF) on AAindex database are used. The two new features are proven to perform better than OE. Third, the data set used here is largely expanded to 1922 samples. Also to improve prediction performance, we conduct parameter optimization for SVM, thus the classifier can obtain better prediction capability. We also fuse the three kinds of features to make sure comprehensive feature representation and improve prediction performance. To effectively evaluate the prediction performance of our method, five parameters, which are much more than previous work, are used to conduct complete comparison. The experimental results of our method show that our method gain better performance than the state of art method. This means that the feature selection combined with feature fusion and classifier parameter optimization can effectively improve HIV-1 cleavage site prediction. Moreover, our work can provide useful help for HIV-1 protease inhibitor developing in the future.

 

Keywords

Dimensionality Reduction; Machine Learning; HIV-1 Protease; Feature Fusion

Conflicts of Interest

The authors declare no conflicts of interest.

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