Abstract
HIV-1 protease has been the subject of intense research for deciphering HIV-1 virus replication process for decades. Knowledge of the substrate specificity of HIV-1 protease will enlighten the way of development of HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, various feature encoding techniques and machine learning algorithms have been used frequently. In this paper, a new feature amino acid encoding scheme is proposed to predict HIV-1 protease cleavage sites. In the proposed method, we combined orthonormal encoding and Taylor’s venn-diagram. We used linear support vector machines as the classifier in the tests. We also analyzed our technique by comparing some feature encoding techniques. The tests are carried out on PR-1625 and PR-3261 datasets. Experimental results show that our amino acid encoding technique leads to better classification performance than other encoding techniques on a standalone classifier.
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Acknowledgments
This work was supported by Sakarya University. BAP Project (Grant 2010-50-02-007).
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Reproducible research: We reported MatLab code and datasets used for obtaining the empirical results in this paper are available at http://dl.dropbox.com/u/70054715/codeHIV1p.zip.
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Gök, M., Özcerit, A.T. A new feature encoding scheme for HIV-1 protease cleavage site prediction. Neural Comput & Applic 22, 1757–1761 (2013). https://doi.org/10.1007/s00521-012-0967-5
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DOI: https://doi.org/10.1007/s00521-012-0967-5