Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas
Gene co-expression network inference from bulk tissue samples often misses cell-type-specific interactions, which can be detected through single-cell gene expression data. However, the noise and sparsity of single-cell data challenge the inference of these networks. We developed scNET, a framework f...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Animal Cells and Systems |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19768354.2025.2472002 |
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