Biomarker Identification in Colorectal Cancer Using Subnetwork Analysis with Feature Selection

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


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.


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



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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