Applications of deep learning in understanding gene regulation

Zhongxiao Li, Elva Gao, Juexiao Zhou, Wenkai Han, Xiaopeng Xu, Xin Gao*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations

Abstract

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

Original languageEnglish (US)
Article number100384
JournalCell Reports Methods
Volume3
Issue number1
DOIs
StatePublished - Jan 23 2023

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

Keywords

  • CP: Systems biology
  • deep learning
  • gene regulation
  • neural network
  • omics

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Genetics
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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