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Biomarker Identification in Colorectal Cancer Using Subnetwork Analysis with Feature Selection

  • Sivakorn KozuevanichEmail author
  • Asawin Meechai
  • Jonathan H. Chan
Conference paper
  • 9 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1149)

Abstract

Gene Sub-Network-based Feature Selection (GSNFS) is an efficient method for handling case-control and multiclass studies for gene sub-network biomarker identification by an integrated analysis of gene expression, gene-set and network data. However, GSNFS has produce considerably high number of sub-network and has not assessed the importance of each sub-network. Recently, we have incorporated 2 feature selection techniques; correlation-based and information gain into the GSNFS workflow to help reduce the number and assess the importance of each individual sub-network. The extended GSNFS method was clearly shown to identify good candidate gene subnetwork markers in lung cancer. In this work, we applied a similar work flow to colorectal cancer. First, the top- and bottom- 5 ranked gene-sets were selected and investigated the classification performance. Similarly, the top-ranked gene-sets showed a better performance than the bottom-ranked gene-sets. The identified top-ranked gene-sets such as TNF-beta and MAPK signaling pathway were known to relate to cancer. In addition, the characteristic of top identified pathway network was further analyzed and visualized. SMAD3, a gene that was reported to be related to cancer by many studies, was mostly found to have the highest neighbor in 4 datasets. The results in this study has confirmed that GSNFS combined with feature selection is very promising as significantly fewer subnetworks were needed to build a classifier and gave a comparable performance to a full dataset classifier.

Keywords

Gene expression analysis Gene-set Classification Colorectal cancer Correlation-based feature selection Information gain feature selection 

Notes

Acknowledgements

The first author would like to acknowledge the graduate scholarship from the Department of Chemical Engineering, KMUTT for funding of his Master study.

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sivakorn Kozuevanich
    • 1
    Email author
  • Asawin Meechai
    • 1
  • Jonathan H. Chan
    • 2
  1. 1.Department of Chemical EngineeringKing Mongkut’s University of Technology ThonburiBangkokThailand
  2. 2.School of Information TechnologyKing Mongkut’s University of Technology ThonburiBangkokThailand

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