Machine learning uncovers cell identity regulator by histone code

Bo Xia, Dongyu Zhao, Guangyu Wang, Min Zhang, Jie Lv, Alin S. Tomoiaga, Yanqiang Li, Xin Wang, Shu Meng, John P. Cooke, Qi Cao, Lili Zhang, Kaifu Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and their expression regulation. Here, we develop CEFCIG, an artificial intelligent framework to uncover CIGs and further define their master regulators. On the basis of machine learning, CEFCIG reveals unique histone codes for transcriptional regulation of reported CIGs, and utilizes these codes to predict CIGs and their master regulators with high accuracy. Applying CEFCIG to 1,005 epigenetic profiles, our analysis uncovers the landscape of regulation network for identity genes in individual cell or tissue types. Together, this work provides insights into cell identity regulation, and delivers a powerful technique to facilitate regenerative medicine.

Original languageEnglish (US)
Article number2696
JournalNature communications
Volume11
Issue number1
DOIs
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Fingerprint

Dive into the research topics of 'Machine learning uncovers cell identity regulator by histone code'. Together they form a unique fingerprint.

Cite this