Protein subcellular localization prediction based on profile alignment and gene ontology

Shibiao Wan, Man Wai Mak, Sun-Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

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 (US)
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
DOIs
StatePublished - Dec 5 2011
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: Sep 18 2011Sep 21 2011

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing

Other

Other21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
CountryChina
CityBeijing
Period9/18/119/21/11

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Keywords

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

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  • Cite this

    Wan, S., Mak, M. W., & Kung, S-Y. (2011). Protein subcellular localization prediction based on profile alignment and gene ontology. In 2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011 [6064613] (IEEE International Workshop on Machine Learning for Signal Processing). https://doi.org/10.1109/MLSP.2011.6064613