iVAE: an interpretable representation learning framework enhances clustering performance for single-cell data
Abstract Background Variational autoencoders (VAEs) serve as essential components in large generative models for extracting latent representations and have gained widespread application in biological domains. Developing VAEs specifically tailored to the unique characteristics of biological data is c...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | BMC Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12915-025-02315-7 |
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