Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method.

Journal: Mathematical biosciences
Published Date: Aug 4, 2018

Abstract

LncRNAs plays an important role in the regulation of gene expression. Identification of cancer-related lncRNAs GO terms and KEGG pathways is great helpful for revealing cancer-related functional biological processes. Therefore, in this study, we proposed a computational method to identify novel cancer-related lncRNAs GO terms and KEGG pathways. By using existing lncRNA database and Max-relevance Min-redundancy (mRMR) method, GO terms and KEGG pathways were evaluated based on their importance on distinguishing cancer-related and non-cancer-related lncRNAs. Finally, GO terms and KEGG pathways with high importance were presented and analyzed. Our literature reviewing showed that the top 10 ranked GO terms and pathways were really related to interpretable tumorigenesis according to recent publications.

Authors

  • Fei Yuan
    Department of Science & Technology, Binzhou Medical University Hospital, Binzhou 256603, Shandong, China. Electronic address: snowhawkyrf@outlook.com.
  • Lin Lu
    School of Economics and Management, Guangxi Normal University, Guilin, China.
  • YuHang Zhang
    Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: yhzhang@sibs.ac.cn.
  • ShaoPeng Wang
    School of Life Sciences, Shanghai University, Shanghai 200444, China.
  • Yu-Dong Cai
    College of Life Science, Shanghai University, Shanghai, People's Republic of China.

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