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Face Recognition via Domain Adaptation and Manifold Distance Metric Learning Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Predicting Multisite Protein Sub-cellular Locations Based on Correlation Coefficient

Authors : Peng Wu, Dong Wang, Xiao-Fang Zhong, Qing Zhao

Published in: Intelligent Computing Theories and Application

Publisher: Springer International Publishing

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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.

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previous chapter Classifying DNA Microarray for Cancer Diagnosis via Method Based on Complex Networks
next chapter Using a Hierarchical Classification Model to Predict Protein Tertiary Structure
Literature
1.
Chen, K., Kurgan, L.A., Ruan, J.: Prediction of protein structural class using novel evolutionary collocation-based sequence representation. J. Comput. Chem. 29(10), 1596–1604 (2008)CrossRef
2.
Du, P., Xu, C.: Predicting multisite protein subcellular locations: progress and challenges. Expert Rev. Proteomics 10(3), 227–237 (2013)CrossRef
3.
Fan, G.L., Li, Q.Z.: Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chous pseudo amino acid composition. J. Theor. Biol. 304, 88–95 (2012)CrossRef
4.
Huang, C., Yuan, J.Q.: Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions. J. Theor. Biol. 335, 205–212 (2013)CrossRef
5.
Li, L., Yu, S., Xiao, W., Li, Y., Li, M., Huang, L., Zheng, X., Zhou, S., Yang, H.: Prediction of bacterial protein subcellular localization by incorporating various features into Chou’s PseAAC and a backward feature selection approach. Biochimie 104, 100–107 (2014)CrossRef
6.
Lin, W.Z., Fang, J.A., Xiao, X., Chou, K.C.: iLoc-animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Mol. BioSyst. 9(4), 634–644 (2013)CrossRef
7.
Mei, S.: Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization. J. Theor. Biol. 293, 121–130 (2012)CrossRefMATH
8.
Shen, H.B., Chou, K.C.: PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem. 373(2), 386–388 (2008)CrossRef
9.
Shen, H.B., Chou, K.C.: Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. J. Biomol. Struct. Dyn. 28(2), 175–186 (2010)CrossRef
10.
Wang, Z., Zou, Q., Jiang, Y., Ju, Y., Zeng, X.: Review of protein subcellular localization prediction. Curr. Bioinform. 9(3), 331–342 (2014)CrossRef
11.
Xiao, X., Wu, Z.C., Chou, K.C.: iLoc-virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J. Theor. Biol. 284(1), 42–51 (2011)CrossRef
12.
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition (in Chinese). Publishing House of Electronic Industry of China, Beijing (1996)
13.
Ji, Z., Wu, D., Zhao, W., et al.: Systemic modeling myeloma-osteoclast interactions under normoxic/hypoxic condition using a novel computational approach. Sci. Rep. 5, 13291 (2015)CrossRef
14.
Wang, B., Zhang, J., Chen, P., et al.: Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features. BMC Bioinform. 14(8), S9 (2013)CrossRef
Metadata
Title
Predicting Multisite Protein Sub-cellular Locations Based on Correlation Coefficient
Authors
Peng Wu
Dong Wang
Xiao-Fang Zhong
Qing Zhao
Copyright Year
2017
Book
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-63311-4

Electronic ISBN: 978-3-319-63312-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63312-1_67

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