Using Chou’s Pseudo Amino Acid Composition and Machine LearningMethod to Predict the Antiviral Peptides

Q3 Computer Science

Open Bioinformatics Journal Pub Date : 2015-03-31 DOI:10.2174/1875036201509010013

M. Zare, H. Mohabatkar, Fatemeh Faramarzi, Majid Mohammad Beigi, M. Behbahani

引用次数: 21

Abstract

Traditional antiviral therapies are expensive, limitedly available, and cause several side effects. Currently, de- signing antiviral peptides is very important, because these peptides interfere with the key stage of virus life cycle. Most of the antiviral peptides are derived from viral proteins for example peptide derived from HIV-1 capsid protein. Because of the importance of these peptides, in this study the concept of pseudo-amino acid composition (PseAAC) and machine learning methods are used to classify or identify antiviral peptides.

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利用Chou的伪氨基酸组成和机器学习方法预测抗病毒肽

传统的抗病毒疗法价格昂贵，可用性有限，并且会产生一些副作用。目前，设计抗病毒肽是非常重要的，因为这些肽干扰病毒生命周期的关键阶段。大多数抗病毒肽来源于病毒蛋白，例如来源于HIV-1衣壳蛋白的肽。由于这些肽的重要性，在本研究中，伪氨基酸组成(PseAAC)的概念和机器学习方法被用于分类或识别抗病毒肽。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Open Bioinformatics Journal Computer Science-Computer Science (miscellaneous)

CiteScore

2.40

自引率

0.00%

发文量

期刊介绍： The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.