Comparison among Feature Encoding Techniques for HIV-1 Protease Cleavage Specificity

Uğur Turhal, Murat Gök, Aykut Durgut
  • Uğur Turhal
    Affiliation not present
  • Aykut Durgut
    Affiliation not present

Abstract

HIV-1 protease which is responsible for the generation of infectious viral particles by cleaving the virus polypeptides, play an indispensable role in the life cycle of HIV-1. Knowledge of the substrate specificity of HIV-1 protease will pave the way of development of efficacious HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, many efforts have been devoted. Last decade, several works have approached the prediction of HIV-1 protease cleavage site problem by applying a number of methods from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective and up-to-date comparison. Here, we have made an extensive study on feature encoding techniques for the problem of HIV-1 protease specificity on diverse machine learning algorithms. Also, for the first time,
we applied OEDICHO technique, which is a combination of orthonormal encoding and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid index database, to predict HIV-1 protease cleavage sites.

Keywords

HIV-1 protease specificity, Feature extraction, Peptide classification, Machine learning algorithms, Amino acids

Full Text:

PDF
Submitted: 2017-03-20 13:08:46
Published: 2015-04-01 00:00:00
Search for citations in Google Scholar
Related articles: Google Scholar
Abstract views:
143

Views:
PDF
70




Copyright (c) 2017 International Journal of Intelligent Systems and Applications in Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
 
© Prof.Dr. Ismail SARITAS 2013-2019     -    Address: Selcuk University, Faculty of Technology 42031 Selcuklu, Konya/TURKEY.