Predicting Multisite Protein Sub-cellular Locations Based on Correlation Coefficient

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10362)

Abstract

With the development of proteomics and cell biology, protein sub-cellular location has become a hot topic in bioinformatics. As the time goes on, more and more researchers make great efforts on studying protein sub-cellular location. But they only do research on single-site protein sub-cellular location. However, some proteins can belong to two or more sub-cellulars. So, we should transfer the line of sight to multisite protein sub-cellular location. In this article, we use Virus-mPLoc data set and choose pseudo amino acid composition and correlation coefficient two effective feature extraction methods. Then, putting these features into multi-label k-nearest neighbor classifier to predict protein sub-cellular location. The experiment proves that this method is reasonable and the precision reached 68.65% through the Jack-knife test.

Keywords

Multisite Pseudo amino acid composition Correlation coefficient Multi-label k-nearest neighbor 
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Notes

Acknowledgment

This research was supported by the National Key Research And Development Program of China (No. 2016YFC0106000), National Natural Science Foundation of China (Grant No. 61302128, 61573166, 61572230, 61671220, 61640218), the Youth Science and Technology Star Program of Jinan City (201406003), the Natural Science Foundation of Shandong Province (ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Peng Wu
    • 1
    • 3
  • Dong Wang
    • 1
    • 3
  • Xiao-Fang Zhong
    • 2
  • Qing Zhao
    • 1
  1. 1.School of Information and EngineeringUniversity of JinanJinanChina
  2. 2.TechnologyShandong Womens UniversityJinanChina
  3. 3.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingShandong Womens UniversityJinanChina

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