CLaSSiNet: A Computational Framework for High-Resolution Classification and Spatial Mapping of Heterogeneous Biological Network Architectures.

Tao Y, Zhou R

DOI: 10.1021/jacsau.5c01775
Published: 2026 Apr 27
Container: JACS Au
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1021/jacsau.5c01775,
  title = {CLaSSiNet: A Computational Framework for High-Resolution Classification and Spatial Mapping of Heterogeneous Biological Network Architectures.},
  author = {Tao Y and Zhou R},
  year = {2026},
  journal = {JACS Au},
  doi = {10.1021/jacsau.5c01775},
  url = {https://doi.org/10.1021/jacsau.5c01775}
}

RIS

TY  - JOUR
TI  - CLaSSiNet: A Computational Framework for High-Resolution Classification and Spatial Mapping of Heterogeneous Biological Network Architectures.
AU  - Tao Y
AU  - Zhou R
PY  - 2026
JO  - JACS Au
DO  - 10.1021/jacsau.5c01775
UR  - https://doi.org/10.1021/jacsau.5c01775
ER  -

APA

Y, T., & R, Z. (2026). CLaSSiNet: A Computational Framework for High-Resolution Classification and Spatial Mapping of Heterogeneous Biological Network Architectures.. JACS Au. https://doi.org/10.1021/jacsau.5c01775

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pubmed · retrieved 2026-09-26T19:44:48.796Z