Computational methods to identify bimodal gene expression and facilitate personalized treatment in cancer patients

Laura Moody, Suparna Mantha, Hong Chen, Yuan Xiang Pan

Research output: Contribution to journalReview article

Abstract

Standard methods for detecting cancer-associated genes rely on comparison of sample means between cancer patients and healthy controls. While such methods have successfully identified several oncogenes and tumor suppressor genes, they neglect to account for heterogeneity within the cancer population. Genetic mutations, translocations, and amplifications are often inconsistent across tumors, and instead they often affect smaller subsets of patients. This concept gives rise to the idea of bimodally expressed genes, or genes that display two modes of expression within one population. Analysis of bimodal gene expression has been explored via a variety of techniques including test statistics and clustering. In this review, we summarize the methodologies used to quantify bimodal gene expression and address the utility of these genes in patient stratification and specialized therapeutics in breast and lung cancer. Finally we discuss the limitations and future directions for bimodal genes in the era of high-throughput sequencing and personalized medicine.

Original languageEnglish (US)
Article number100001
JournalJournal of Biomedical Informatics: X
Volume1
DOIs
StatePublished - Mar 2019

Keywords

  • Bimodality
  • Clustering
  • Mixture-model
  • Personalized medicine
  • Prognosis
  • Unsupervised learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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