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.
ACS Style
Pramod Bharadwaj Chandrashekar; Hai Chen; Matthew Lee; Navid Ahmadinejad; Li Liu. DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements.
Computational and Structural Biotechnology Journal 2024, 23, 679 -687.
AMA Style
Pramod Bharadwaj Chandrashekar, Hai Chen, Matthew Lee, Navid Ahmadinejad, Li Liu. DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements.
Computational and Structural Biotechnology Journal. 2024; 23
():679-687.
Chicago/Turabian Style
Pramod Bharadwaj Chandrashekar; Hai Chen; Matthew Lee; Navid Ahmadinejad; Li Liu. 2024.
"DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements."
Computational and Structural Biotechnology Journal 23, no. :
679-687.
