Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging
Abstract Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large omic datasets, in order to study biological...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89646-1 |
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| author | Zhi-Peng Li Zhaozhen Du De-Shuang Huang Andrew E. Teschendorff |
| author_facet | Zhi-Peng Li Zhaozhen Du De-Shuang Huang Andrew E. Teschendorff |
| author_sort | Zhi-Peng Li |
| collection | DOAJ |
| description | Abstract Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large omic datasets, in order to study biological aging at the molecular level. We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI. We apply this framework to one of the largest single-cell transcriptomic datasets encompassing over a million immune cells from 981 donors, revealing a ribosomal gene subnetwork, whose expression correlates with age independently of cell-type. Application of the same DL-XAI framework to DNA methylation data of sorted monocytes reveals an epigenetically deregulated inflammatory response pathway whose activity increases with age. We show that the ribosomal module and inflammatory pathways would not have been discovered had we used more standard machine-learning methods. In summary, the computational deep learning framework presented here illustrates how deep learning when combined with explainable AI tools, can reveal novel biological insights into the complex process of aging. |
| format | Article |
| id | doaj-art-cbbaea73ed1e404d8157a1b512e44313 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cbbaea73ed1e404d8157a1b512e443132025-08-20T03:05:01ZengNature PortfolioScientific Reports2045-23222025-02-0115111810.1038/s41598-025-89646-1Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in agingZhi-Peng Li0Zhaozhen Du1De-Shuang Huang2Andrew E. Teschendorff3Ningbo Institute of Digital Twin, Eastern Institute of TechnologyCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of SciencesNingbo Institute of Digital Twin, Eastern Institute of TechnologyCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of SciencesAbstract Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large omic datasets, in order to study biological aging at the molecular level. We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI. We apply this framework to one of the largest single-cell transcriptomic datasets encompassing over a million immune cells from 981 donors, revealing a ribosomal gene subnetwork, whose expression correlates with age independently of cell-type. Application of the same DL-XAI framework to DNA methylation data of sorted monocytes reveals an epigenetically deregulated inflammatory response pathway whose activity increases with age. We show that the ribosomal module and inflammatory pathways would not have been discovered had we used more standard machine-learning methods. In summary, the computational deep learning framework presented here illustrates how deep learning when combined with explainable AI tools, can reveal novel biological insights into the complex process of aging.https://doi.org/10.1038/s41598-025-89646-1Deep-learningAIGraph-level representation learningAgingSingle-cellEpigenetics |
| spellingShingle | Zhi-Peng Li Zhaozhen Du De-Shuang Huang Andrew E. Teschendorff Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging Scientific Reports Deep-learning AI Graph-level representation learning Aging Single-cell Epigenetics |
| title | Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging |
| title_full | Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging |
| title_fullStr | Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging |
| title_full_unstemmed | Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging |
| title_short | Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging |
| title_sort | interpretable deep learning of single cell and epigenetic data reveals novel molecular insights in aging |
| topic | Deep-learning AI Graph-level representation learning Aging Single-cell Epigenetics |
| url | https://doi.org/10.1038/s41598-025-89646-1 |
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