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Open Access

EPIMR: Prediction of Enhancer-Promoter Interactions by Multi-Scale ResNet on Image Representation

Qiaozhen Meng1, Yinuo Lyu2, Xiaoqing Peng3, Junhai Xu1( ), Jijun Tang4( ), Fei Guo5( )
School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
Aeronautical Information Service Center of the Civil Aviation Administration of China (AISC.ATMB.CAAC), Beijing 100015, China
Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410038, China
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Abstract

Prediction of enhancer-promoter interactions (EPIs) is key to regulating gene expression and diagnosing genetic diseases. Due to limited resolution, biological experiments perform not as well as expected while precisely identifying specific interactions, giving rise to computational biology approaches. Many EPI predictors have been developed, but their prediction accuracy still needs to be enhanced. Here, we design a new model named EPIMR to identify enhancer-promoter interactions. First, Hilbert Curve is utilized to represent sequences to images to preserve the position and spatial information. Second, a multi-scale residual neural network (ResNet) is used to learn the distinguishing features of different abstraction levels. Finally, matching heuristics are adopted to concatenate the learned features of enhancers and promoters, which pays attention to their potential interaction information. Experimental results on six cell lines indicate that EPIMR performs better than existing methods, with higher area under the precision-recall curve (AUPR) and area under the receiver operating characteristic (AUROC) results on benchmark and under-sampling datasets. Furthermore, our model is pre-trained on all cell lines, which improves not only the transferability of cross-cell line prediction, but also cell line-specific prediction ability. In conclusion, our method serves as a valuable technical tool for predicting enhancer-promoter interactions, contributing to the understanding of gene transcription mechanisms. Our code and results are available at https://github.com/guofei-tju/EPIMR.

Keywords

enhancer-promoter interactionsHilbert Curvemulti-scale residual neural network (ResNet)

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 668-681
DOI: 10.26599/BDMA.2024.9020018
Cite this article:
Meng Q, Lyu Y, Peng X, et al. EPIMR: Prediction of Enhancer-Promoter Interactions by Multi-Scale ResNet on Image Representation. Big Data Mining and Analytics, 2024, 7(3): 668-681. https://doi.org/10.26599/BDMA.2024.9020018

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Received: 28 December 2023
Revised: 01 February 2024
Accepted: 18 March 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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