Yang Donghong, Peng Xin, Zheng Senlin, Peng Shenglan
School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China.
Sci Rep. 2025 Feb 7;15(1):4576. doi: 10.1038/s41598-025-88477-4.
Autoimmune Diseases are a complex group of diseases caused by the immune system mistakenly attacking body tissues. Their etiology involves multiple factors such as genetics, environmental factors, and abnormalities in immune cells, making prediction and treatment challenging. T cells, as a core component of the immune system, play a critical role in the human immune system and have a significant impact on the pathogenesis of autoimmune diseases. Several studies have demonstrated that T-cell receptors (TCRs) may be involved in the pathogenesis of various autoimmune diseases, which provides strong theoretical support and new therapeutic targets for the prediction and treatment of autoimmune diseases. This study focuses on the prediction of several autoimmune diseases mediated by T cells, and proposes two models: one is the AutoY model based on convolutional neural networks, and the other is the LSTMY model, a bidirectional LSTM network model that integrates the attention mechanism. Experimental results show that both models exhibit good performance in the prediction of the four autoimmune diseases, with the AutoY model performing slightly better in comparison. In particular, the average area under the ROC curve (AUC) of the AutoY model exceeded 0.93 in the prediction of all the diseases, and the AUC value reached 0.99 in two diseases, type 1 diabetes and multiple sclerosis. These results demonstrate the high accuracy, stability, and good generalization ability of the two models, which makes them promising tools in the field of autoimmune disease prediction and provides support for the use of the TCR bank for the noninvasive detection of autoimmune disease non-invasive detection is supported.
自身免疫性疾病是由免疫系统错误地攻击身体组织而引起的一组复杂疾病。其病因涉及多种因素，如遗传因素、环境因素和免疫细胞异常，这使得预测和治疗具有挑战性。T细胞作为免疫系统的核心组成部分，在人体免疫系统中发挥着关键作用，对自身免疫性疾病的发病机制有重大影响。多项研究表明，T细胞受体（TCRs）可能参与各种自身免疫性疾病的发病机制，这为自身免疫性疾病的预测和治疗提供了有力的理论支持和新的治疗靶点。本研究聚焦于几种由T细胞介导的自身免疫性疾病的预测，并提出了两种模型：一种是基于卷积神经网络的AutoY模型，另一种是整合了注意力机制的双向LSTM网络模型LSTMY。实验结果表明，这两种模型在四种自身免疫性疾病的预测中均表现出良好的性能，相比之下，AutoY模型表现略优。特别是，AutoY模型在所有疾病预测中的ROC曲线下面积（AUC）平均值超过0.93，在1型糖尿病和多发性硬化症这两种疾病中AUC值达到0.99。这些结果证明了这两种模型具有高准确性、稳定性和良好的泛化能力，使其成为自身免疫性疾病预测领域有前景的工具，并为使用TCR库进行自身免疫性疾病的无创检测提供了支持。
