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Original Research ARTICLE

Front. Genet. | doi: 10.3389/fgene.2020.00869

Learning Cell-Type-Specific Gene Regulation Mechanisms by Multi-Attention Based Deep Learning with Regulatory Latent Space Provisionally accepted The final, formatted version of the article will be published soon. Notify me

  • 1Seoul National University, South Korea

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

Keywords: gene regulation mechanism, gene regulatory network, multi-omics, deep learning, cell-type-specific

Received: 07 Feb 2020; Accepted: 16 Jul 2020.

Copyright: © 2020 Kang, Lee, Lee and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Sun Kim, Seoul National University, Seoul, 151-742, South Korea,