XP-GCN: Extreme learning machines and parallel graph convolutional networks for high-throughput prediction of blood-brain barrier penetration based on feature fusion.
- DOI
- 10.1016/j.compbiolchem.2025.108755
- Published
- 2026 Feb
- Container
- Computational biology and chemistry
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.compbiolchem.2025.108755,
title = {XP-GCN: Extreme learning machines and parallel graph convolutional networks for high-throughput prediction of blood-brain barrier penetration based on feature fusion.},
author = {Pala MA},
year = {2026},
journal = {Computational biology and chemistry},
doi = {10.1016/j.compbiolchem.2025.108755},
url = {https://doi.org/10.1016/j.compbiolchem.2025.108755}
}RIS
TY - JOUR TI - XP-GCN: Extreme learning machines and parallel graph convolutional networks for high-throughput prediction of blood-brain barrier penetration based on feature fusion. AU - Pala MA PY - 2026 JO - Computational biology and chemistry DO - 10.1016/j.compbiolchem.2025.108755 UR - https://doi.org/10.1016/j.compbiolchem.2025.108755 ER -
APA
MA, P. (2026). XP-GCN: Extreme learning machines and parallel graph convolutional networks for high-throughput prediction of blood-brain barrier penetration based on feature fusion.. Computational biology and chemistry. https://doi.org/10.1016/j.compbiolchem.2025.108755
Source records
- pubmed · retrieved 2026-09-26T21:49:29.753Z