Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106999
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Wan, S | en_US |
dc.creator | Mak, MW | en_US |
dc.creator | Kung, SY | en_US |
dc.date.accessioned | 2024-06-07T00:59:31Z | - |
dc.date.available | 2024-06-07T00:59:31Z | - |
dc.identifier.issn | 1535-3893 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106999 | - |
dc.language.iso | en | en_US |
dc.publisher | American Chemical Society | en_US |
dc.rights | © 2016 American Chemical Society | en_US |
dc.rights | This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Proteome Research, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jproteome.6b00686. | en_US |
dc.subject | Linear neighborhood propagation | en_US |
dc.subject | Multi-label classification | en_US |
dc.subject | Protein subchloroplast localization | en_US |
dc.subject | Split amino-acid composition | en_US |
dc.subject | Transductive learning | en_US |
dc.title | Ensemble linear neighborhood propagation for predicting subchloroplast localization of multi-location proteins | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 4755 | en_US |
dc.identifier.epage | 4762 | en_US |
dc.identifier.volume | 15 | en_US |
dc.identifier.issue | 12 | en_US |
dc.identifier.doi | 10.1021/acs.jproteome.6b00686 | en_US |
dcterms.abstract | In the postgenomic era, the number of unreviewed protein sequences is remarkably larger and grows tremendously faster than that of reviewed ones. However, existing methods for protein subchloroplast localization often ignore the information from these unlabeled proteins. This paper proposes a multi-label predictor based on ensemble linear neighborhood propagation (LNP), namely, LNP-Chlo, which leverages hybrid sequence-based feature information from both labeled and unlabeled proteins for predicting localization of both single- and multi-label chloroplast proteins. Experimental results on a stringent benchmark dataset and a novel independent dataset suggest that LNP-Chlo performs at least 6% (absolute) better than state-of-the-art predictors. This paper also demonstrates that ensemble LNP significantly outperforms LNP based on individual features. For readers’ convenience, the online Web server LNP-Chlo is freely available at http://bioinfo.eie.polyu.edu.hk/LNPChloServer/. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of proteome research, 2 Dec. 2016, v. 15, no. 12, p. 4755-4762 | en_US |
dcterms.isPartOf | Journal of proteome research | en_US |
dcterms.issued | 2016-12-02 | - |
dc.identifier.scopus | 2-s2.0-85000360223 | - |
dc.identifier.eissn | 1535-3907 | en_US |
dc.description.validate | 202405 bcch | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0789 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6700974 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Mak_Ensemble_Linear_Neighborhood.pdf | Pre-Published version | 1.39 MB | Adobe PDF | View/Open |
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