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Gene Extraction Based on Sparse Singular Value Decomposition

  • Xiangzhen Kong
  • Jinxing Liu
  • Chunhou Zheng
  • Junliang Shang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

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.

Keywords

Singular value decomposition Gene extraction Sparse constraint Gene Ontology 

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiangzhen Kong
    • 1
  • Jinxing Liu
    • 1
    • 2
  • Chunhou Zheng
    • 1
  • Junliang Shang
    • 1
  1. 1.School of Information Science and EngineeringQufu Normal UniversityRizhaoChina
  2. 2.Bio-Computing Research Center, Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina

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