用免疫细胞受体序列精准识别自身免疫病，准确率超97%。
Classification of autoimmune diseases from Peripheral blood TCR repertoires by multimodal multi-instance learning
- 基于多实例学习框架，融合序列特征与注意力机制
- SLE和RA诊断AUC分别达98.95%和97.76%
- 可区分疾病特异性基因，适合临床辅助诊断
T细胞受体（TCR）谱型蕴含自身免疫病的关键免疫信号，但其临床应用受限于序列稀疏和低检出率。我们开发了EAMil，一种多实例深度学习框架，利用TCR测序数据对系统性红斑狼疮（SLE）和类风湿性关节炎（RA）进行高精度诊断。通过整合PrimeSeq特征提取、ESMonehot编码及增强门控注意力机制，模型在两项疾病上的表现达到当前最优水平，AUC分别为98.95%（SLE）和97.76%（RA）。EAMil识别出与疾病相关的基因，与已有差异分析结果的吻合度超过90%，并有效区分疾病特异性TCR基因。该模型在多类别分类中表现稳健，可结合SLEDAI评分对SLE患者按病情严重程度分层，并诊断其损伤部位，同时有效控制年龄、性别等混杂因素。这一可解释的免疫受体分析框架为自身免疫病的检测与分类提供了新视角，具有广泛临床应用潜力。
原文摘要 · Abstract (English)
T cell receptor (TCR) repertoires encode critical immunological signatures for autoimmune diseases, yet their clinical application remains limited by sequence sparsity and low witness rates. We developed EAMil, a multi-instance deep learning framework that leverages TCR sequencing data to diagnose systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) with exceptional accuracy. By integrating PrimeSeq feature extraction with ESMonehot encoding and enhanced gate attention mechanisms, our model achieved state-of-the-art performance with AUCs of 98.95% for SLE and 97.76% for RA. EAMil successfully identified disease-associated genes with over 90% concordance with established differential analyses and effectively distinguished disease-specific TCR genes. The model demonstrated robustness in classifying multiple disease categories, utilizing the SLEDAI score to stratify SLE patients by disease severity as well as to diagnose the site of damage in SLE patients, and effectively controlling for confounding factors such as age and gender. This interpretable framework for immune receptor analysis provides new insights for autoimmune disease detection and classification with broad potential clinical applications across immune-mediated conditions.
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