Mem-ADSVM: A two-layer multi-label predictor for identifying multi-functional types of membrane proteins

Shibiao Wan, Man Wai Mak, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

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 languageEnglish (US)
Pages (from-to)32-42
Number of pages11
JournalJournal of Theoretical Biology
Volume398
DOIs
StatePublished - Jun 7 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Keywords

  • Adaptive-decision scheme
  • Gene ontology
  • Membrane protein type prediction
  • Multi-label classification
  • Two-layer classification

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