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A New Subcellular Localization Predictor for Human Proteins Considering the Correlation of Annotation Features and Protein Multi-localization

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Book cover Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

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Abstract

Identifying a protein’s subcellular localization is meaningful to understand the function of the protein. While experimental method to identify the subcellular localization of proteins will cost a lot of time, it is necessary to utilize computational approaches for dealing with large scale proteins of unknown location. Current predictors mostly consider the annotation-based features but few of them take their correlation into account. Moreover, most of predictors can only deal with single-locational proteins, while a lot of proteins bear multi-locational characteristics, which play important roles in many biological processes. In this paper, we propose a novel prediction method, which extracts features from prior biological knowledge by considering the correlation between annotation terms. The new method can also deal with the multi-localization problem. We compared the performance of the proposed method with other predictors on four datasets. The result shows that our method is outperform than others.

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Author information

Authors and Affiliations

  1. Key Laboratory of System Control and Information Processing, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai, China

    Hang Zhou & Hong-Bin Shen

  2. Department of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yang Yang

Authors
  1. Hang Zhou
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  2. Yang Yang
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  3. Hong-Bin Shen
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Corresponding authors

Correspondence to Yang Yang or Hong-Bin Shen .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Zhou, H., Yang, Y., Shen, HB. (2016). A New Subcellular Localization Predictor for Human Proteins Considering the Correlation of Annotation Features and Protein Multi-localization. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_41

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

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