Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy

Research output: Contribution to journalArticlepeer-review

Abstract

We report an experimental design issue in recent machine learning formulations of the enhancer-promoter interaction problem arising from the fact that many enhancer-promoter pairs share features. Cross-fold validation schemes which do not correctly separate these feature sharing enhancer-promoter pairs into one test set report high accuracy, which is actually arising from high training set accuracy and a failure to properly evaluate generalization performance. Cross-fold validation schemes which properly segregate pairs with shared features show markedly reduced ability to predict enhancer-promoter interactions from epigenomic state. Parameter scans with multiple models indicate that local epigenomic features of individual pairs of enhancers and promoters cannot distinguish those pairs that interact from those which do with high accuracy, suggesting that additional information is required to predict enhancer-promoter interactions.

Original languageEnglish (US)
Article numbere1006625
JournalPLoS computational biology
Volume14
Issue number12
DOIs
StatePublished - Dec 2018

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy'. Together they form a unique fingerprint.

Cite this