title: Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins.
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/ .
authors: Department of Electronic and Information Engineering, The Hong Kong Polytechnic University , Hong Kong SAR, China.
authors: Shibiao
authors: S
authors: Wan
authors: Department of Electronic and Information Engineering, The Hong Kong Polytechnic University , Hong Kong SAR, China.
authors: Man-Wai
authors: MW
authors: Mak
authors: Department of Electrical Engineering, Princeton University , New Jersey 08540, United States.
authors: Sun-Yuan
authors: SY
authors: Kung
mesh_terms: D060365:Chloroplast Proteins / Q000378:metabolism*; D002736:Chloroplasts / Q000737:chemistry / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D013347:Subcellular Fractions / Q000737:chemistry*
chemical_list: D060365:Chloroplast Proteins
keywords: linear neighborhood propagation; multi-label classification; protein subchloroplast localization; split amino-acid composition; transductive learning