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http://hdl.handle.net/10397/106999
Title: | Ensemble linear neighborhood propagation for predicting subchloroplast localization of multi-location proteins | Authors: | Wan, S Mak, MW Kung, SY |
Issue Date: | 2-Dec-2016 | Source: | Journal of proteome research, 2 Dec. 2016, v. 15, no. 12, p. 4755-4762 | 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/. | Keywords: | Linear neighborhood propagation Multi-label classification Protein subchloroplast localization Split amino-acid composition Transductive learning |
Publisher: | American Chemical Society | Journal: | Journal of proteome research | ISSN: | 1535-3893 | EISSN: | 1535-3907 | DOI: | 10.1021/acs.jproteome.6b00686 | Rights: | © 2016 American Chemical Society 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. |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Mak_Ensemble_Linear_Neighborhood.pdf | Pre-Published version | 1.39 MB | Adobe PDF | View/Open |
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