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Gene Extraction Based on Sparse Singular Value Decomposition

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Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9771))

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Abstract

In this paper, we develop a new feature extraction method based on sparse singular value decomposition (SSVD). We apply SSVD algorithm to select the characteristic genes from Colorectal Cancer (CRC) genomic dataset, and then the differentially expressed genes obtained are evaluated by the tools based on Gene Ontology. As a gene extraction method, SSVD is also compared with some existing feature extraction methods such as independent component analysis (ICA), the p-norm robust feature extraction (PREE) and sparse principal component analysis (SPCA). The experimental results show that SSVD method outperforms the existing algorithms.

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Acknowledgement

This work was supported in part by the grants of the National Science Foundation of China, Nos. 61572284, 61502272, 61572283; Shenzhen Municipal Science and Technology Innovation Council, No. JCYJ20140417172417174; Natural Science Foundation of Shandong Province, No. BS2014DX004.

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Authors and Affiliations

  1. School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China

    Xiangzhen Kong, Jinxing Liu, Chunhou Zheng & Junliang Shang

  2. Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China

    Jinxing Liu

Authors
  1. Xiangzhen Kong
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  2. Jinxing Liu
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  3. Chunhou Zheng
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  4. Junliang Shang
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Corresponding author

Correspondence to Jinxing Liu .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari , Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong , North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Kong, X., Liu, J., Zheng, C., Shang, J. (2016). Gene Extraction Based on Sparse Singular Value Decomposition. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_28

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  • DOI: https://doi.org/10.1007/978-3-319-42291-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42290-9

  • Online ISBN: 978-3-319-42291-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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