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 un
labeled 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 un
labeled 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/.