Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees

Yi Zhang, Xiaofei Zhang, Andrew N. Lane, Teresa W.M. Fan, Jinze Liu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Metabolic reprogramming is a hallmark of cancer. In cancer cells, transcription factors (TFs) govern metabolic reprogramming through abnormally increasing or decreasing the transcription rate of metabolic enzymes, which provides cancer cells growth advantages and concurrently leads to the altered metabolic phenotypes observed in many cancers. Consequently, targeting TFs that govern metabolic reprogramming can be highly effective for novel cancer therapeutics. In this paper, we present TFmeta, a machine learning approach to uncover TFs that govern reprogramming of cancer metabolism. Our approach achieves the state-of-the-art performance in reconstructing relations between TFs and their target genes on public benchmark datasets. Leveraging TF binding profiles inferred from genome-wide ChIP-seq experiments and 150 RNA-seq samples from 75 paired cancerous and non-cancerous human lung tissues, our approach predicted 19 key TFs that may be the major regulators of the gene expression changes of metabolic enzymes of the central metabolic pathway glycolysis, which may underlie the dysregulation of glycolysis in non-small-cell lung cancer patients.

Original languageEnglish
Article number8781833
Pages (from-to)1528-1536
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume24
Issue number5
DOIs
StatePublished - May 2020

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Machine learning
  • gene regulatory network
  • lung cancer
  • metabolic reprogramming
  • transcription factor

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management

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