1Department of Pharmacy and Department of Computational Science, National University of Singapore, Republic of Singapore, 117543. 2Department of Biotechnology, Zhejiang University, Hang Zhou, Zhejiang Province, P. R. China, 310029. 3Shanghai Center for Bioinformatics Technology, Shanghai, P. R. China, 201203.
Abstract: Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clusteringbased, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying diffi culties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.
PDF (497.55 KB PDF FORMAT)
RIS citation (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)
BibTex citation (BIBDESK, LATEX)
Bioinformatics and Biology Insights helps to reach all people with the latest results on research which directly helps them and with their needs. Three of our co-authors are from Burkina Faso, the malaria holoendemic region our research is based on, and serving as motivation for all our efforts for better treatment of malaria. It is good to be social and it is good to promote science world-wide through open access.
All authors are surveyed after their articles are published. Authors are asked to rate their experience in a variety of areas, and their responses help us to monitor our performance. Presented here are their responses in some key areas. No 'poor' or 'very poor' responses were received; these are represented in the 'other' category.See Our Results
Copyright © 2013 Libertas Academica Ltd (except open access articles and accompanying metadata and supplementary files.)