Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61144
Title: Mem-ADSVM : a two-layer multi-label predictor for identifying multi-functional types of membrane proteins
Authors: Wan, S
Mak, MW 
Kung, SY
Keywords: Adaptive-decision scheme
Gene ontology
Membrane protein type prediction
Multi-label classification
Two-layer classification
Issue Date: 2016
Publisher: Academic Press
Source: Journal of theoretical biology, 2016, v. 398, p. 32-42 How to cite?
Journal: Journal of theoretical biology 
Abstract: Identifying membrane proteins and their multi-functional types is an indispensable yet challenging topic in proteomics and bioinformatics. However, most of the existing membrane-protein predictors have the following problems: (1) they do not predict whether a given protein is a membrane protein or not, (2) they are limited to predicting membrane proteins with single-label functional types but ignore those with multi-functional types; and (3) there is still much room for improvement for their performance. To address these problems, this paper proposes a two-layer multi-label predictor, namely Mem-ADSVM, which can identify membrane proteins (Layer I) and their multi-functional types (Layer II). Specifically, given a query protein, its associated gene ontology (GO) information is retrieved by searching a compact GO-term database with its homologous accession number. Subsequently, the GO information is classified by a binary support vector machine (SVM) classifier to determine whether it is a membrane protein or not. If yes, it will be further classified by a multi-label multi-class SVM classifier equipped with an adaptive-decision (AD) scheme to determine to which functional type(s) it belongs. Experimental results show that Mem-ADSVM significantly outperforms state-of-the-art predictors in terms of identifying both membrane proteins and their multi-functional types. This paper also suggests that the two-layer prediction architecture is better than the one-layer for prediction performance. For reader's convenience, the Mem-ADSVM server is available online at http://bioinfo.eie.polyu.edu.hk/MemADSVMServer/.
URI: http://hdl.handle.net/10397/61144
ISSN: 0022-5193
DOI: 10.1016/j.jtbi.2016.03.013
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