title: Learning to Discover Regulatory Elements for Gene Expression Prediction
summary: 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/).
paper_authors._id: 67bced139d37199a15263ec5
paper_authors.name: Xingyu Su
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T09:40:30.431Z
paper_authors._id: 662d70df52e194d5d495a567
paper_authors.avatarUrl: /avatars/efc6cf8a4f39140cc683343d6df0580b.svg
paper_authors.fullname: Xingyu Su
paper_authors.type: user
paper_authors.user: xingyusu
paper_authors._id: 67bced139d37199a15263ec6
paper_authors.name: Haiyang Yu
paper_authors._id: 67bced139d37199a15263ec7
paper_authors.name: Degui Zhi
paper_authors._id: 67bced139d37199a15263ec8
paper_authors.name: Shuiwang Ji
paper_title: Learning to Discover Regulatory Elements for Gene Expression Prediction
paper_summary: 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/).
paper_ai_summary: Seq2Exp network enhances gene expression prediction by decomposing epigenomic signals and DNA sequences with an information bottleneck, outperforming existing methods.
paper_ai_keywords: Sequence to Expression Seq2Exp epigenomic signals DNA sequence causal relationship information bottleneck Beta distribution gene expression prediction MACS3 AIRS library