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

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

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

Content Provider	ACM Digital Library
Author	Wan, Shibiao Kung, Sun-Yuan Mak, Man-Wai
Copyright Year	2016
Description	Author Affiliation: Department of Electrical Engineering Princeton University(Mak, Man-Wai; Department of electronic and information engineering the hong kong polytechnic university (wan, Shibiao; Kung, Sun-Yuan))
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/.
Starting Page	706
Ending Page	718
Page Count	13
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2015.2474407
Volume Number	13
Issue Number	4
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Membrane protein type prediction Elastic net Gene ontology Interpretable predictor Multi-label classification
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets

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