Enhancer-promoter interactions (EPIs) regulate the expression of specific genes in cells, which help facilitate understanding of gene regulation, cell differentiation and disease mechanisms. EPI identification approaches through wet experiments are often costly and time-consuming, leading to the design of high-efficiency computational methods is in demand. In this paper, we propose a deep neural network-based method named EPIHC to predict E nhancer- P romoter I nteractions with H ybrid features and C ommunicative learning. EPIHC extracts enhancer and promoter sequence-derived features using convolutional neural networks (CNN), and then we design a communicative learning module to capture the communicative information between enhancer and promoter sequences. Besides, EPIHC takes the genomic features of enhancers and promoters into account, incorporating with the sequence-derived features to predict EPIs. The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveals that the communicative learning module can bring explicit information about EPIs, which is ignored by CNN, and provide explainability about EPIs to some degree. Moreover, we consider two strategies to improve the performances of EPIHC in the cross-cell line prediction, and experimental results show that EPIHC constructed on some cell lines can exhibit good performances for other cell lines. The codes and data are available at https://github.com/BioMedicalBigDataMiningLab/EPIHC .

中文翻译：

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EPIHC：通过使用混合特征和交流学习改进增强子-启动子交互预测

增强子-启动子相互作用 (EPI) 调节细胞中特定基因的表达，这有助于促进对基因调控、细胞分化和疾病机制的理解。通过湿实验进行 EPI 识别的方法通常成本高且耗时，因此需要设计高效的计算方法。在本文中，我们提出了一种名为 EPIHC 的基于深度神经网络的方法来预测增强剂- 发起人我与混合特征和交际学习。EPIHC 使用卷积神经网络 (CNN) 提取增强子和启动子序列衍生特征，然后我们设计了一个交流学习模块来捕获增强子和启动子序列之间的交流信息。此外，EPIHC 将增强子和启动子的基因组特征考虑在内，结合序列衍生特征来预测 EPI。计算实验表明，EPIHC 在基准数据集和染色体分裂数据集上优于现有最先进的 EPI 预测方法，研究表明，交流学习模块可以带来关于 EPI 的显式信息，而这些信息被 CNN 忽略，并在一定程度上提供有关 EPI 的可解释性。而且，我们考虑了两种策略来提高 EPIHC 在跨细胞系预测中的性能，实验结果表明，在某些细胞系上构建的 EPIHC 可以对其他细胞系表现出良好的性能。代码和数据可在https://github.com/BioMedicalBigDataMiningLab/EPIHC .