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Classification of autoimmune diseases from Peripheral blood TCR repertoires by multimodal multi-instance learning

Created by
  • Haebom

Author

Ruihao Zhang, Fei Ye, Dandan Meng, Yixuan Huang, Maochen, Xiao Liu

Outline

We developed a multi-instance deep learning framework called EAMil to diagnose systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) with high accuracy using TCR sequencing data. By integrating PrimeSeq feature extraction, ESMonehot encoding, and an improved gated attention mechanism, we achieved state-of-the-art performance with AUCs of 98.95% for SLE and 97.76% for RA. EAMil successfully identified disease-related genes with over 90% agreement with conventional differential analysis and effectively distinguished disease-specific TCR genes. We demonstrated robustness in classifying SLE patients according to disease severity and lesion site diagnosis using the SLEDAI score, effectively controlling for confounding factors such as age and gender. This interpretable immune receptor analysis framework provides new insights into autoimmune disease detection and classification with broad potential clinical applications across the spectrum of immune-mediated diseases.

Takeaways, Limitations

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Takeaways:
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We present a novel deep learning model, EAMil, that achieves excellent accuracy (AUC 98.95% for SLE, 97.76% for RA) in the diagnosis of SLE and RA.
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It shows high agreement with existing difference analysis and can identify disease-related genes and distinguish disease-specific TCR genes.
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Diagnosis of disease severity and damage area possible using SLEDAI score.
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Effectively controls for confounding factors such as age and gender.
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Suggesting broad clinical applicability to various immune-mediated diseases.
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Limitations:
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This paper lacks specific references to the __T8463_____ of the EAMil model. Further research is needed to further validate the generalization performance of the model, its extendibility to other autoimmune diseases, and its clinical applicability. In addition, a detailed description of the size and diversity of the datasets used is needed.
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