TY - JOUR
T1 - DeepCORE
T2 - An interpretable multi-view deep neural network model to detect co-operative regulatory elements
AU - Chandrashekar, Pramod Bharadwaj
AU - Chen, Hai
AU - Lee, Matthew
AU - Ahmadinejad, Navid
AU - Liu, Li
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/12
Y1 - 2024/12
N2 - Gene transcription is an essential process involved in all aspects of cellular functions with significant impact on biological traits and diseases. This process is tightly regulated by multiple elements that co-operate to jointly modulate the transcription levels of target genes. To decipher the complicated regulatory network, we present a novel multi-view attention-based deep neural network that models the relationship between genetic, epigenetic, and transcriptional patterns and identifies co-operative regulatory elements (COREs). We applied this new method, named DeepCORE, to predict transcriptomes in various tissues and cell lines, which outperformed the state-of-the-art algorithms. Furthermore, DeepCORE contains an interpreter that extracts the attention values embedded in the deep neural network, maps the attended regions to putative regulatory elements, and infers COREs based on correlated attentions. The identified COREs are significantly enriched with known promoters and enhancers. Novel regulatory elements discovered by DeepCORE showed epigenetic signatures consistent with the status of histone modification marks.
AB - Gene transcription is an essential process involved in all aspects of cellular functions with significant impact on biological traits and diseases. This process is tightly regulated by multiple elements that co-operate to jointly modulate the transcription levels of target genes. To decipher the complicated regulatory network, we present a novel multi-view attention-based deep neural network that models the relationship between genetic, epigenetic, and transcriptional patterns and identifies co-operative regulatory elements (COREs). We applied this new method, named DeepCORE, to predict transcriptomes in various tissues and cell lines, which outperformed the state-of-the-art algorithms. Furthermore, DeepCORE contains an interpreter that extracts the attention values embedded in the deep neural network, maps the attended regions to putative regulatory elements, and infers COREs based on correlated attentions. The identified COREs are significantly enriched with known promoters and enhancers. Novel regulatory elements discovered by DeepCORE showed epigenetic signatures consistent with the status of histone modification marks.
KW - Cooperative regulatory elements
KW - Deep learning
KW - Epigenetics
KW - Gene regulation
UR - http://www.scopus.com/inward/record.url?scp=85181918051&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85181918051&partnerID=8YFLogxK
U2 - 10.1016/j.csbj.2023.12.044
DO - 10.1016/j.csbj.2023.12.044
M3 - Article
AN - SCOPUS:85181918051
SN - 2001-0370
VL - 23
SP - 679
EP - 687
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -