Please use this identifier to cite or link to this item: https://doi.org/10.2174/138920308783565697
Title: Homology-free prediction of functional class of proteins and peptides by support vector machines
Authors: Zhu, F. 
Han, L.Y. 
Chen, X.
Lin, H.H.
Ong, S.
Xie, B.
Zhang, H.L.
Chen, Y.Z. 
Keywords: Machine learning method
Peptide
Peptide function
Protein family
Protein function
Protein function prediction
Protein sequence
Support vector machine
Issue Date: 2008
Citation: Zhu, F.,Han, L.Y.,Chen, X.,Lin, H.H.,Ong, S.,Xie, B.,Zhang, H.L.,Chen, Y.Z. (2008). Homology-free prediction of functional class of proteins and peptides by support vector machines. Current Protein and Peptide Science 9 (1) : 70-95. ScholarBank@NUS Repository. https://doi.org/10.2174/138920308783565697
Abstract: Protein and peptide sequences contain clues for functional prediction. A challenge is to predict sequences that show low or no homology to proteins or peptides of known function. A machine learning method, support vector machines (SVM), has recently been explored for predicting functional class of proteins and peptides from sequence-derived properties irrespective of sequence similarity, which has shown impressive performance for predicting a wide range of protein and peptide classes including certain low- and non- homologous sequences. This method serves as a new and valuable addition to complement the extensively-used alignment-based, clustering-based, and structure-based functional prediction methods. This article evaluates the strategies, current progresses, reported prediction performances, available software tools, and underlying difficulties in using SVM for predicting the functional class of proteins and peptides. © 2008 Bentham Science Publishers Ltd.
Source Title: Current Protein and Peptide Science
URI: http://scholarbank.nus.edu.sg/handle/10635/106659
ISSN: 13892037
DOI: 10.2174/138920308783565697
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

9
checked on Oct 16, 2018

Page view(s)

29
checked on Mar 10, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.