iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.
- DOI
- 10.1371/journal.pcbi.1014727
- Published
- 2026 Aug
- Container
- PLoS computational biology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pcbi.1014727,
title = {iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.},
author = {Guo H and Wu T and Wang W and Jiang Y and Li G and Nie L and Jia Y and Quan L and Huang M and Lyu Q},
year = {2026},
journal = {PLoS computational biology},
doi = {10.1371/journal.pcbi.1014727},
url = {https://doi.org/10.1371/journal.pcbi.1014727}
}RIS
TY - JOUR TI - iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data. AU - Guo H AU - Wu T AU - Wang W AU - Jiang Y AU - Li G AU - Nie L AU - Jia Y AU - Quan L AU - Huang M AU - Lyu Q PY - 2026 JO - PLoS computational biology DO - 10.1371/journal.pcbi.1014727 UR - https://doi.org/10.1371/journal.pcbi.1014727 ER -
APA
H, G., T, W., W, W., Y, J., G, L., L, N., Y, J., L, Q., M, H., & Q, L. (2026). iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.. PLoS computational biology. https://doi.org/10.1371/journal.pcbi.1014727
Source records
- pubmed · retrieved 2026-11-10T09:36:09.621Z