Learning to Discover Regulatory Elements for Gene Expression Prediction
We consider the problem of predicting gene expressions from DNA sequences. A
key challenge of this task is to find the regulatory elements that control gene
expressions. Here, we introduce Seq2Exp, a Sequence to Expression network
explicitly designed to discover and extract regulatory elements that drive
target gene expression, enhancing the accuracy of the gene expression
prediction. Our approach captures the causal relationship between epigenomic
signals, DNA sequences and their associated regulatory elements. Specifically,
we propose to decompose the epigenomic signals and the DNA sequence conditioned
on the causal active regulatory elements, and apply an information bottleneck
with the Beta distribution to combine their effects while filtering out
non-causal components. Our experiments demonstrate that Seq2Exp outperforms
existing baselines in gene expression prediction tasks and discovers
influential regions compared to commonly used statistical methods for peak
detection such as MACS3. The source code is released as part of the AIRS
library (https://github.com/divelab/AIRS/).
67bced139d37199a15263ec5
Xingyu Su
claimed_verified
2025-02-25T09:40:30.431Z
662d70df52e194d5d495a567
/avatars/efc6cf8a4f39140cc683343d6df0580b.svg
Xingyu Su
user
xingyusu
67bced139d37199a15263ec6
Haiyang Yu
67bced139d37199a15263ec7
Degui Zhi
67bced139d37199a15263ec8
Shuiwang Ji
Learning to Discover Regulatory Elements for Gene Expression Prediction
We consider the problem of predicting gene expressions from DNA sequences. A
key challenge of this task is to find the regulatory elements that control gene
expressions. Here, we introduce Seq2Exp, a Sequence to Expression network
explicitly designed to discover and extract regulatory elements that drive
target gene expression, enhancing the accuracy of the gene expression
prediction. Our approach captures the causal relationship between epigenomic
signals, DNA sequences and their associated regulatory elements. Specifically,
we propose to decompose the epigenomic signals and the DNA sequence conditioned
on the causal active regulatory elements, and apply an information bottleneck
with the Beta distribution to combine their effects while filtering out
non-causal components. Our experiments demonstrate that Seq2Exp outperforms
existing baselines in gene expression prediction tasks and discovers
influential regions compared to commonly used statistical methods for peak
detection such as MACS3. The source code is released as part of the AIRS
library (https://github.com/divelab/AIRS/).
Seq2Exp network enhances gene expression prediction by decomposing epigenomic signals and DNA sequences with an information bottleneck, outperforming existing methods.
Sequence to Expression Seq2Exp epigenomic signals DNA sequence causal relationship information bottleneck Beta distribution gene expression prediction MACS3 AIRS library