DeepCKI:一个基于变分图自编码器预测细胞-细胞因子相互作用的生物信息学模型

细胞因子(cytokine)是一类由免疫细胞和某些非免疫细胞合成和分泌的信号分子，在免疫系统中通过结合相应受体调节细胞生长、分化和调控免疫应答。目前研究多侧重于通过实验方法检测细胞因子和受体的相互作用来研究细胞间的通讯网络，但存在实验周期长、设备要求高和成本高等不足。因此，有必要通过计算方法来加快对细胞-细胞因子相互作用(cell-cytokine interactions, CKI)的系统研究。本文提出一种基于变分图自编码器(variational graph auto-encoder, VGAE)预测细胞-细胞因子相互作用的深度学习模型——DeepCKI。该模型可有效融合蛋白质相互作用网络和不同类型的蛋白质特征，充分挖掘网络拓扑结构和节点属性中的有效信息，实现对细胞-细胞因子相互作用的高效预测。与变分自编码和深度神经网络方法相比，采用图结构设计的DeepCKI表现出了最优的预测性能。DeepCKI模型对4种不同类型细胞-细胞因子相互作用的ROC曲线下面积均高于0.8,模型具有一定的鲁棒性和有效性。预测打分排名前100的细胞-细胞因子相互作用中，有36对已被最新发表文献验证，表明该模...

DeepCKI,a Bioinformatics Model for Predicting Cell-Cytokine Interactions Based on Variational Graph Auto-Encoder

Cytokines are a class of signaling molecules that are synthesized and secretedby immune cells and certain non-immune cells, and regulate cell growth, differentiation, and immune response by binding to corresponding receptors in the immune system. Most of the current studies focus on investigating the intercellular communication network using experimental methods to detect the interaction between cytokines and receptors, but there are short comings such as long experimental cycles, high equipment requirements, and high costs. Therefore, it is necessary to accelerate the systematic study of cell-cytokine interactions (CKIs) through computational methods. In this paper, we propose DeepCKI, a deep learning model based on variational graph auto-encoder (VGAE) to predict cell-cytokine interactions, which can effectively fuse protein interactions and different types of protein features, and fully exploit the effective information in network topology and node properties to achieve efficient prediction of cell-cytokine interactions. Compared with variational auto-encoder and deep neural network methods, DeepCKI with graph structure designs exhibits optimal prediction performance. The AUC values of the DeepCKI model for four different types of cell-cytokine interactions are higher than 0.8, and the model has certain robustness and effectiveness. Among the top 100 cell-cytokine interactions scored for prediction, 36 pairs have been validated by the latest published literature, indicating that the model can discover new cell-cytokine interactions.