Jun 8, 2018

Integrating distal and proximal information to predict gene expression via a densely con-nected convolutional neural network

BioRxiv : the Preprint Server for Biology
Wanwen ZengRui Jiang


Motivation: Interactions among such cis-regulatory elements as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epige-nomic information, most of them overlook long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, a novel high-throughput experimental approach named HiChIP has been developed and generating compre-hensive data on high-resolution interactions between promoters and distal enhancers. On the other hand, plenty of studies have suggested that deep learning achieves state-of-the-art perfor-mance in epigenomic signal prediction, and thus promoting the understanding of regulatory ele-ments. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately model gene expression. Results: We propose DeepExpression, a densely connected convolutional neural network to pre-dict gene expression using both promoter sequences and enhancer-promoter interactions. We demonstrate that our model consistently outperforms base...Continue Reading

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Mentioned in this Paper

Biological Neural Networks
Genomic Stability
Cross Validation
SPT6 Protein
Gene Expression
Neural Stem Cells

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