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/.
Original language | English (US) |
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Pages (from-to) | 32-42 |
Number of pages | 11 |
Journal | Journal of Theoretical Biology |
Volume | 398 |
DOIs | |
State | Published - Jun 7 2016 |
All Science Journal Classification (ASJC) codes
- General Immunology and Microbiology
- Applied Mathematics
- General Biochemistry, Genetics and Molecular Biology
- General Agricultural and Biological Sciences
- Statistics and Probability
- Modeling and Simulation
Keywords
- Adaptive-decision scheme
- Gene ontology
- Membrane protein type prediction
- Multi-label classification
- Two-layer classification