Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107267
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Wan, S | - |
dc.creator | Mak, MW | - |
dc.creator | Kung, SY | - |
dc.date.accessioned | 2024-06-13T01:05:00Z | - |
dc.date.available | 2024-06-13T01:05:00Z | - |
dc.identifier.issn | 1545-5963 | - |
dc.identifier.uri | http://hdl.handle.net/10397/107267 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication S. Wan, M. -W. Mak and S. -Y. Kung, "Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 4, pp. 706-718, July-Aug. 2016 is available at https://doi.org/10.1109/TCBB.2015.2474407. | en_US |
dc.subject | Elastic net | en_US |
dc.subject | Gene ontology | en_US |
dc.subject | Interpretable predictor | en_US |
dc.subject | Membrane protein type prediction | en_US |
dc.subject | Multi-label classification | en_US |
dc.title | Mem-mEN : predicting multi-functional types of membrane proteins by interpretable elastic nets | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 706 | - |
dc.identifier.epage | 718 | - |
dc.identifier.volume | 13 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.1109/TCBB.2015.2474407 | - |
dcterms.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/. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE/ACM transactions on computational biology and bioinformatics, July-Aug. 2016, v. 13, no. 4, p. 706-718 | - |
dcterms.isPartOf | IEEE/ACM transactions on computational biology and bioinformatics | - |
dcterms.issued | 2016-07 | - |
dc.identifier.scopus | 2-s2.0-84982085504 | - |
dc.identifier.pmid | 26336143 | - |
dc.identifier.eissn | 1557-9964 | - |
dc.description.validate | 202403 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0848 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong PolyU | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6667090 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Mak_Mem-Men_Predicting_Multi-Functional.pdf | Pre-Published version | 1.5 MB | Adobe PDF | View/Open |
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