Protein subcellular localization prediction based on profile alignment and gene ontology

Shibiao Wan, Man Wai Mak, Sun Yuan Kung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

19 Citations (Scopus)


The functions of proteins are closely related to their subcellular locations. Computational methods are required to replace the laborious and time-consuming experimental processes for proteomics research. This paper proposes combining homology-based profile alignment methods and functional-domain based Gene Ontology (GO) methods to predict the subcellular locations of proteins. The feature vectors constructed by these two methods are recognized by support vector machine (SVM) classifiers, and their scores are fused to enhance classification performance. The paper also investigates different approaches to constructing the GO vectors based on the GO terms returned from InterProScan. The results demonstrate that the GO methods are comparable to profile-alignment methods and overshadow those based on amino-acid compositions. Also, the fusion of these two methods can outperform the individual methods.
Original languageEnglish
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
Publication statusPublished - 5 Dec 2011
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: 18 Sep 201121 Sep 2011


Conference21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011


  • Gene Ontology
  • InterProScan
  • PairProSVM
  • Profile Alignment
  • Protein subcellular localization
  • Support vector machines

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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