Learning Cell-Type-Specific Gene Regulation Mechanisms by Multi-Attention Based Deep Learning With Regulatory Latent Space

Frontiers in Genetics

Sangseon Lee¹  Minji Kang¹  Sun Kim²  Dohoon Lee³ 

[1] Bioinformatics Institute, Seoul National University, Seoul, South Korea;Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul, South Korea;Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea;

关键词: gene regulation mechanism;    gene regulatory network;    multi-omics;    deep learning;    cell-type-specific;   

DOI  :  10.3389/fgene.2020.00869

来源: DOAJ

【 摘 要 】

Epigenetic gene regulation is a major control mechanism of gene expression. Most existing methods for modeling control mechanisms of gene expression use only a single epigenetic marker and very few methods are successful in modeling complex mechanisms of gene regulations using multiple epigenetic markers on transcriptional regulation. In this paper, we propose a multi-attention based deep learning model that integrates multiple markers to characterize complex gene regulation mechanisms. In experiments with 18 cell line multi-omics data, our proposed model predicted the gene expression level more accurately than the state-of-the-art model. Moreover, the model successfully revealed cell-type-specific gene expression control mechanisms. Finally, the model was used to identify genes enriched for specific cell types in terms of their functions and epigenetic regulation.

【 授权许可】

Unknown