Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65599
Title: Ensemble linear neighborhood propagation for predicting subchloroplast localization of multi-location proteins
Authors: Wan, S
Mak, MW
Kung, SY
Keywords: Linear neighborhood propagation
Multi-label classification
Protein subchloroplast localization
Split amino-acid composition
Transductive learning
Issue Date: 2016
Publisher: American Chemical Society
Source: Journal of proteome research, 2016, v. 15, no. 12, p. 4755-4762 How to cite?
Journal: Journal of proteome research 
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/.
URI: http://hdl.handle.net/10397/65599
ISSN: 1535-3893
DOI: 10.1021/acs.jproteome.6b00686
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