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Random Walk Based Global Feature for Disease Gene Identification

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Pattern Recognition (CCPR 2016)

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

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Abstract

Disease gene identification is of great significance for the treatment of genetic disorders. In recent years, the rapid development of high-throughput sequencing technologies has brought great revolution for disease gene identification methods. Network-based methods are now the most efficient component for disease gene identification, while the most of current methods pay only attention to the local topological attributes regardless of the global distribution. In this paper, we proposed to apply the random walk algorithm to extract global features for each gene and finally used binary logistic regression model to identify whether a gene belongs to the given disease. We also integrate the local features and global features into a complex feature vector to improve the identification performance. The experimental results show that the global feature is of great efficiency for disease gene identification. We organize the global feature into different kinds of feature vectors and we can get higher AUC scores than other state-of-the-art methods for all these feature vectors.

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Acknowledgment

This work is surported by JCYJ20140904154645958 and CXZZ20140904154910774.

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Authors and Affiliations

  1. Shen Zhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Lezhen Wei, Shuai Wu, Jian Zhang & Yong Xu

Authors
  1. Lezhen Wei
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  2. Shuai Wu
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  3. Jian Zhang
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  4. Yong Xu
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Corresponding author

Correspondence to Shuai Wu .

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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Wei, L., Wu, S., Zhang, J., Xu, Y. (2016). Random Walk Based Global Feature for Disease Gene Identification. 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_38

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_38

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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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