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A Hybrid Tumor Gene Selection Method with Laplacian Score and Correlation Analysis

  • Bo LiEmail author
  • Xiao-Hui Lei
  • Yang Hu
  • Xiao-Long Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

In the proposed method, Laplacian criteria is firstly introduced to sort the genes as their descending scores. And then, correlation analysis is applied to select those pathogenic genes from the sorted sequence to reduce the redundancy. At last, SVM classifier is used to predict the class labels of the optimal gene subset. Compared to some other related gene selection methods such as Fisher score and Laplacian score, Experimental results on four standard datasets have shown the stability and efficiency of the proposed method.

Keywords

Correlation analysis Laplacian score Tumor gene expressive data Gene selection 

Notes

Acknowledgement

This work was partly supported by the grants of Natural Science Foundation of China (61273303, 61273225, 61373109 and 61572381), China Postdoctoral Science Foundation (20100470613 and 201104173), Natural Science Foundation of Hubei Province (2010CDB03302), the Research Foundation of Education Bureau of Hubei Province (Q20121115).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bo Li
    • 1
    • 2
    • 3
    Email author
  • Xiao-Hui Lei
    • 1
    • 2
  • Yang Hu
    • 1
    • 2
  • Xiao-Long Zhang
    • 1
    • 2
  1. 1.School of Computer Science of TechnologyWuhan University of Science of TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhanChina
  3. 3.School of Electronics and Information EngineeringTongji UniversityShanghaiChina

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