Please use this identifier to cite or link to this item:
|Title:||Advances in exploration of machine learning methods for predicting functional class and interaction profiles of proteins and peptides irrespective of sequence homology||Authors:||Cui, J.
|Keywords:||Machine learning method
Protein function prediction
Support vector machine
|Issue Date:||May-2007||Citation:||Cui, J., Han, L., Lin, H., Tang, Z., Ji, Z., Cao, Z., Li, Y., Chen, Y. (2007-05). Advances in exploration of machine learning methods for predicting functional class and interaction profiles of proteins and peptides irrespective of sequence homology. Current Bioinformatics 2 (2) : 95-112. ScholarBank@NUS Repository. https://doi.org/10.2174/157489307780618222||Abstract:||Various computational methods have been used for predicting protein function from clues contained in protein sequence. A particular challenge is the functional prediction of proteins that show low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from a variety of sequence-derived structural and physicochemical properties independent of sequence similarity, which showed promising potential for a broad spectrum of proteins including those that show low and no similarity to other proteins. These methods can thus be explored as potential tools to complement similarity-based, clustering-based and structure-based methods for predicting protein function. This article reviews the strategies, algorithms, current progresses, available software and web-servers, and underlying difficulties in using machine learning methods for predicting the functional class of proteins and peptides, and protein-protein interactions. The reported prediction performances in the application of these methods are also presented. © 2007 Bentham Science Publishers Ltd.||Source Title:||Current Bioinformatics||URI:||http://scholarbank.nus.edu.sg/handle/10635/106607||ISSN:||15748936||DOI:||10.2174/157489307780618222|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jun 17, 2019
WEB OF SCIENCETM
checked on Jun 10, 2019
checked on May 24, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.