Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets

Shibiao Wan, Man Wai Mak, Sun Yuan Kung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

Abstract

Membrane proteins play important roles in various biological processes within organisms. Predicting the functional types of membrane proteins is indispensable to the characterization of membrane proteins. Recent studies have extended to predicting single-and multi-type membrane proteins. However, existing predictors perform poorly and more importantly, they are often lack of interpretability. To address these problems, this paper proposes an efficient predictor, namely Mem-mEN, which can produce sparse and interpretable solutions for predicting membrane proteins with single-and multi-label functional types. Given a query membrane protein, its associated gene ontology (GO) information is retrieved by searching a compact GO-term database with its homologous accession number, which is subsequently classified by a multi-label elastic net (EN) classifier. Experimental results show that Mem-mEN significantly outperforms existing state-of-the-art membrane-protein predictors. Moreover, by using Mem-mEN, 338 out of more than 7,900 GO terms are found to play more essential roles in determining the functional types. Based on these 338 essential GO terms, Mem-mEN can not only predict the functional type of a membrane protein, but also explain why it belongs to that type. For the reader's convenience, the Mem-mEN server is available online at http://bioinfo.eie.polyu.edu.hk/MemmENServer/.
Original languageEnglish
Article number7229308
Pages (from-to)706-718
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume13
Issue number4
DOIs
Publication statusPublished - 1 Jul 2016

Keywords

  • elastic net
  • gene ontology
  • interpretable predictor
  • Membrane protein type prediction
  • multi-label classification

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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