白细胞介素6（简称IL-6）是一种高多效性的糖蛋白因子，可以调节先天性免疫、适应性免疫以及代谢的各个方面，包括糖酵解、脂肪酸氧化和氧化磷酸化等。许多研究已证明，病毒感染患者体内的IL-6表达和释放量显著增加，并且与疾病的严重程度呈正相关，因此，识别IL-6诱导肽并探究其作用机制，对于开发免疫治疗以及疾病严重程度生物标志物具有重要的意义。目前对于IL-6诱导肽的识别大多采用传统的机器学习方法，特征选择与提取较为复杂，且需要依赖领域专家知识。鉴于此，该文提出一种基于图神经网络的IL-6诱导肽预测方法SFGNN-IL6。该方法根据所预测的IL-6诱导肽的结构特征，通过阈值筛选距离信息构建邻接矩阵，结合氨基酸的编码方式（One-hot编码、位置编码和BLOSUM62编码）提取氨基酸节点特征并进行图表示；然后，采用图注意力机制层和图卷积神经网络层，由双通道分别提取多视角特征，既关注节点权重的更新，也考虑节点信息的更新；最后，将两类特征进行融合，用于IL-6诱导肽的分类。实验结果验证了该方法的有效性。

Interleukin-6 (IL-6) is a highly multifunctional glycoprotein factor that can regulate both innate and adaptive immunity as well as various aspects of metabolism, including glycolysis, fatty acid oxidation and oxidative phosphorylation. Many studies have shown that the expression and release of IL-6 in patients infected with viruses significantly increase, and are positively correlated with the severity of the disease. Therefore, identifying IL-6 inducing peptides and exploring their action mechanisms are very important for developing immune therapies and dia-gnostic biomarker for the severity of diseases. Currently, the identification methods of IL-6 inducing peptides mostly use traditional machine learning, in which feature selection and extraction are rather complex, and field expert knowledge are required. In view of this problem, this paper proposes a novel graph neural network-based prediction method of IL-6 inducing peptides named SFGNN-IL6. In this method, the predicted structural characteristics of IL-6 inducing peptides are used to construct the adjacency matrix by screening the distance information according to the threshold, and the node features of amino acids are extracted using One-hot encoding, position encoding and BLOSUM62 encoding, and are then graph-represented. Moreover, graph attention mechanism layers and graph convolutional neural network layers are used as dual channels to separately extract features, considering both the update of node weights and the update of node information. Finally, the two types of features are fused for the classification of IL-6 inducing peptides. Experimental results validate that the proposed method is effective.