An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data

Murakami, Ken; Iida, Keita; Okada, Mariko

doi:https://doi.org/10.1111/gtc.70000

An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data

Murakami, Ken; Iida, Keita; Okada, Mariko

Genes to Cells, 2025, e70000

アクセス数:301件（2025-07-18 20:52 集計）

固定URL: https://hdl.handle.net/11094/100542

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タイトル	An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data
著者	著者名 Murakami, Ken
	NRID 1000010709653 1000010709653 ORCID 0000-0002-1076-830X 0000-0002-1076-830X researchmap permalink 7000005567 7000005567 Scopus Author ID 35409372400 35409372400 著者名 Iida, Keita
	NRID 1000010342833 1000010342833 ORCID 0000-0002-6210-8223 0000-0002-6210-8223 researchmap permalink mariko_okada mariko_okada Scopus Author ID 57194562293 57194562293 著者名 Okada, Mariko
キーワード等	attention-based neural network
	cis-regulatory elements
	deep learning
	enhancers
	explainable artificial intelligence
	gene regulation
	intra-tumor heterogeneity
	single-cell analysis
	single-cell ATAC-seq
	single-cell multiome
内容	Murakami K., Iida K., Okada M. An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data. Genes to Cells 30, e70000 (2025); https://doi.org/10.1111/gtc.70000.
抄録	Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activity at the single-cell level is essential. However, current analytical methods can only capture the average behavior of cREs in cell populations, thereby obscuring cell-specific variations. To address this limitation, we proposed an attention-based deep neural network framework that integrates DNA sequences, genomic distances, and single-cell multi-omics data to detect cREs and their activities in individual cells. Our model shows higher accuracy in identifying cREs within single-cell multi-omics data from healthy human peripheral blood mononuclear cells than other existing methods. Furthermore, it clusters cells more precisely based on predicted cRE activities, enabling a finer differentiation of cell states. When applied to publicly available single-cell data from patients with glioma, the model successfully identified tumor-specific SOX2 activity. Additionally, it revealed the heterogeneous activation of the ZEB1 transcription factor, a regulator of epithelial-to-mesenchymal transition-related genes, which conventional methods struggle to detect. Overall, our model is a powerful tool for detecting cRE regulation at the single-cell level, which may contribute to revealing drug resistance mechanisms in cell sub-populations.
出版者	John Wiley and Sons Inc
収録物名	Genes to Cells
ページ	e70000
刊行年月	2025-03-01
言語	英語
ハンドルURL	https://hdl.handle.net/11094/100542
PISSN	13569597
eISSN	13652443
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1111/gtc.70000
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	Genes to Cells. 2025, p. e70000
DC.title	An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data
DC.creator	Murakami, Ken
	Iida, Keita
	Okada, Mariko
DC.publisher	John Wiley and Sons Inc
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-03-01
DC.identifier	https://doi.org/10.1111/gtc.70000
DC.subject	attention-based neural network
	cis-regulatory elements
	deep learning
	enhancers
	explainable artificial intelligence
	gene regulation
	intra-tumor heterogeneity
	single-cell analysis
	single-cell ATAC-seq
	single-cell multiome
DC.description	Murakami K., Iida K., Okada M. An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data. Genes to Cells 30, e70000 (2025); https://doi.org/10.1111/gtc.70000.
DCTERMS.abstract	Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activity at the single-cell level is essential. However, current analytical methods can only capture the average behavior of cREs in cell populations, thereby obscuring cell-specific variations. To address this limitation, we proposed an attention-based deep neural network framework that integrates DNA sequences, genomic distances, and single-cell multi-omics data to detect cREs and their activities in individual cells. Our model shows higher accuracy in identifying cREs within single-cell multi-omics data from healthy human peripheral blood mononuclear cells than other existing methods. Furthermore, it clusters cells more precisely based on predicted cRE activities, enabling a finer differentiation of cell states. When applied to publicly available single-cell data from patients with glioma, the model successfully identified tumor-specific SOX2 activity. Additionally, it revealed the heterogeneous activation of the ZEB1 transcription factor, a regulator of epithelial-to-mesenchymal transition-related genes, which conventional methods struggle to detect. Overall, our model is a powerful tool for detecting cRE regulation at the single-cell level, which may contribute to revealing drug resistance mechanisms in cell sub-populations.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DC.format	100542
citation_title	An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data
citation_author	Murakami, Ken
	Iida, Keita
	Okada, Mariko
citation_publisher	John Wiley and Sons Inc
citation_language	英語
citation_date	2025-03-01
citation_journal_title	Genes to Cells
citation_firstpage	e70000
citation_doi	https://doi.org/10.1111/gtc.70000
citation_public_url	https://hdl.handle.net/11094/100542

An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data

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