Abstract：
Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study findings from SVCV-infected EPC cells suggest that MEFFGRN can predict novel gene regulatory relationships.
摘要：
基因调控网络（GRN）对于理解有机分子机制和过程至关重要。通过鲤鱼病毒春季病毒血症（SVCV）感染在鲤鱼上皮瘤（EPC）细胞中构建GRN有助于了解免疫调节机制，从而增强鲤鱼的生存能力。尽管已经使用了许多计算方法来推断GRN，缺乏预测SVCV感染后EPC细胞GRN的专门方法。此外,大多数现有方法主要关注基因表达特征，忽略已知GRN中的有价值的网络结构信息。在这项研究中,我们提出了一种新的有监督深度神经网络，MEFFGRN(基于矩阵增强和特征融合的基因调控网络推断方法)，以准确预测SVCV感染后EPC细胞的GRN。MEFFGRN同时考虑了已知GRN的基因表达数据和网络结构信息，并引入了矩阵增强方法来解决已知GRN的稀疏性问题。提取更丰富的网络结构信息。为了优化CNN（卷积神经网络）在图像处理中的优势，基因表达和增强的GRN数据分别转换为每个基因对的直方图图像。随后，将这些直方图分别输入CNN进行训练，以获得相应的基因表达和网络结构特征。此外，引入了特征融合机制，全面整合了基因表达和网络结构特征。这种集成考虑了每个特征及其交互信息的特殊性，导致在融合过程中更全面和精确的特征表示。来自真实世界和基准数据集的实验结果表明，与最先进的计算方法相比，MEFFGRN具有竞争力。此外，来自SVCV感染的EPC细胞的研究结果表明，MEFFGRN可以预测新的基因调控关系。
