Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine

Emre Arslan, Jonathan Schulz, Kunal Rai

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations


The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.

Original languageEnglish (US)
Article number188588
JournalBiochimica et Biophysica Acta - Reviews on Cancer
Issue number2
StatePublished - Dec 2021


  • Cancer
  • Chromatin
  • Deep learning
  • Epigenomics
  • Machine learning

ASJC Scopus subject areas

  • Oncology
  • Genetics
  • Cancer Research


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