Deep learning-based prediction of autoimmune diseases
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-88477-4 |
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| _version_ | 1823862476845875200 |
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| author | Donghong Yang Xin Peng Senlin Zheng Shenglan Peng |
| author_facet | Donghong Yang Xin Peng Senlin Zheng Shenglan Peng |
| author_sort | Donghong Yang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4feeef7632c444748f6ffec15839da9f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4feeef7632c444748f6ffec15839da9f2025-02-09T12:32:10ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-88477-4Deep learning-based prediction of autoimmune diseasesDonghong Yang0Xin Peng1Senlin Zheng2Shenglan Peng3School of Information Engineering, Jingdezhen Ceramic UniversitySchool of Information Engineering, Jingdezhen Ceramic UniversityThird Institute of Oceanography, Ministry of Natural ResourcesSchool of Information Engineering, Jingdezhen Ceramic UniversityAbstract 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.https://doi.org/10.1038/s41598-025-88477-4 |
| spellingShingle | Donghong Yang Xin Peng Senlin Zheng Shenglan Peng Deep learning-based prediction of autoimmune diseases Scientific Reports |
| title | Deep learning-based prediction of autoimmune diseases |
| title_full | Deep learning-based prediction of autoimmune diseases |
| title_fullStr | Deep learning-based prediction of autoimmune diseases |
| title_full_unstemmed | Deep learning-based prediction of autoimmune diseases |
| title_short | Deep learning-based prediction of autoimmune diseases |
| title_sort | deep learning based prediction of autoimmune diseases |
| url | https://doi.org/10.1038/s41598-025-88477-4 |
| work_keys_str_mv | AT donghongyang deeplearningbasedpredictionofautoimmunediseases AT xinpeng deeplearningbasedpredictionofautoimmunediseases AT senlinzheng deeplearningbasedpredictionofautoimmunediseases AT shenglanpeng deeplearningbasedpredictionofautoimmunediseases |