Please use this identifier to cite or link to this item: https://doi.org/10.2174/157489307780618222
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.
Han, L. 
Lin, H.
Tang, Z.
Ji, Z.
Cao, Z.
Li, Y.
Chen, Y. 
Keywords: Machine learning method
Neural network
Peptide prediction
Protein function prediction
Protein-protein interaction
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

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