Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins

Shibiao Wan, Man Wai Mak, Sun Yuan Kung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


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/.
Original languageEnglish
Pages (from-to)4755-4762
Number of pages8
JournalJournal of Proteome Research
Issue number12
Publication statusPublished - 2 Dec 2016


  • linear neighborhood propagation
  • multi-label classification
  • protein subchloroplast localization
  • split amino-acid composition
  • transductive learning

ASJC Scopus subject areas

  • Biochemistry
  • General Chemistry


Dive into the research topics of 'Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins'. Together they form a unique fingerprint.

Cite this