R-GAT: cancer document classification leveraging graph-based residual network for scenarios with limited data
- DOI
- 10.1038/s41598-026-39894-6
- Published
- 2026-02-17
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-39894-6,
title = {R-GAT: cancer document classification leveraging graph-based residual network for scenarios with limited data},
author = {Elias Hossain and Tasfia Nuzhat and Shamsul Masum and Shahram Rahimi and Noorbakhsh Amiri Golilarz},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-39894-6},
url = {https://doi.org/10.1038/s41598-026-39894-6}
}RIS
TY - JOUR TI - R-GAT: cancer document classification leveraging graph-based residual network for scenarios with limited data AU - Elias Hossain AU - Tasfia Nuzhat AU - Shamsul Masum AU - Shahram Rahimi AU - Noorbakhsh Amiri Golilarz PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-39894-6 UR - https://doi.org/10.1038/s41598-026-39894-6 ER -
APA
Hossain, E., Nuzhat, T., Masum, S., Rahimi, S., & Golilarz, N. A. (2026). R-GAT: cancer document classification leveraging graph-based residual network for scenarios with limited data. Scientific Reports. https://doi.org/10.1038/s41598-026-39894-6
Source records
- crossref · retrieved 2026-09-25T20:46:17.068Z