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  1. Home
  3. Kansformerepi: A Deep Learning Framework Integrating Kan And Transformer For Predicting Enhancer-promoter Interactions.
  1. Home
  3. Kansformerepi: A Deep Learning Framework Integrating Kan And Transformer For Predicting Enhancer-promoter Interactions.

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KansformerEPI: a deep learning framework integrating KAN and transformer for predicting enhancer-promoter

Tianjiao Zhang1, Saihong Shao1, Hongfei Zhang1

  • 1College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin 150040, China.

Briefings in Bioinformatics
|June 14, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

KansformerEPI accurately predicts enhancer-promoter interactions (EPIs) across multiple cell types using a novel deep learning model. This approach improves scalability and prediction accuracy for gene regulation studies and disease research.

Keywords:
KANdeep learningenhancer–promoter interactionstransformer

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Enhancer-promoter interactions (EPIs) are crucial for gene regulation and understanding disease mechanisms.
  • Current computational methods for genome-wide EPI prediction often lack a multi-cell line perspective and fail to capture complex nonlinear feature relationships.
  • Existing models are typically limited to single cell lines, hindering scalability and broad applicability.

Purpose of the Study:

  • To develop a global enhancer-promoter interaction (EPI) prediction model applicable across multiple cell types.
  • To improve the accuracy and scalability of computational EPI prediction by effectively modeling nonlinear relationships between epigenetic and sequence features.
  • To provide a versatile tool for understanding transcriptional regulation and disease mechanisms across diverse cellular contexts.

Main Methods:

  • Developed KansformerEPI, a novel global EPI prediction model integrating KAN and Transformer architectures within an encoder.
  • The Kansformer encoder captures nonlinear relationships among epigenetic and sequence features for enhanced prediction.
  • Applied KansformerEPI for cross-tissue EPI prediction across various cell types, including HMEC, IMR90, K562, and NHEK.

Main Results:

  • KansformerEPI demonstrated superior accuracy and stability in predicting enhancer-promoter interactions compared to existing methods like TransEPI, TargetFinder, and SPEID.
  • The model successfully achieved cross-tissue prediction, highlighting its scalability and reduced dependency on tissue-specific datasets.
  • Experimental results validated the model's effectiveness across diverse biological datasets.

Conclusions:

  • KansformerEPI offers a scalable and accurate solution for predicting enhancer-promoter interactions across multiple cell types.
  • The model's ability to capture nonlinear feature relationships advances computational approaches in gene regulation research.
  • This work provides valuable insights into transcriptional regulation and disease mechanisms, applicable across various tissues and reducing the need for extensive, cell-type-specific datasets.