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Predicting Subcellular Localization of Multiple Sites Proteins

  • Dong Wang
  • Wenzheng Bao
  • Yuehui ChenEmail author
  • Wenxing HeEmail author
  • Luyao Wang
  • Yuling Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

Accurate classification on protein subcellular localization plays an important role in Bioinformatics. An increasingly evidences demonstrate that a variety of classification methods have been employed in this field. This research adopts feature fusion method to extract the information of the protein subcellular. Several types of features are employed in this protein coding method, which include amino acid index distribution, the stereo-chemical properties of amino acids and the information for local sequence of amino acids. On base of this feature combination method, flexible neutral tree (FNT) is employed to predict multiplex protein subcellular locations. The overall accuracy rate of using flexible neutral tree as prediction algorithm may reach a better result.

Keywords

Amino acid index distribution (AAID) Pseudo amino acid composition (PseAAC) Stereo-chemical properties (SP) Flexible neutral tree (FNT) 

Notes

Acknowledgment

This research was partially supported by the Youth Project of National Natural Science Fund (Grant No. 61302128), Shandong Provincial Natural Science Foundation, China, under Grant ZR2015JL025, the Youth Science and Technology Star Program of Jinan City (201406003), the Natural Science Foundation of Shandong Province (ZR2011FL022, ZR2013FL002), the Scientific Research Fund of Jinan University (XKY1410, XKY1411), the Program for Scientific research innovation team in Colleges and Universities of Shandong Province (2012–2015), and the Shandong Provincial Key Laboratory of Network Based Intelligent Computing.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Institute of Machine Learning and Systems Biology, College of Electronics and Information EngineeringTongji UniversityShanghaiChina
  3. 3.School of Biological Science and TechnologyUniversity of JinanJinanChina

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