xLSTM-based deep learning model for predicting anticancer Activity of pyrano[3,2-a]phenazine hybrid derivatives: Proof of concept
- DOI
- 10.1016/j.compbiolchem.2026.109431
- Published
- 2027-02
- Container
- Computational Biology and Chemistry
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.compbiolchem.2026.109431,
title = {xLSTM-based deep learning model for predicting anticancer Activity of pyrano[3,2-a]phenazine hybrid derivatives: Proof of concept},
author = {Mohamed Aly Saad Aly and Aya I. Maiyza and Hala S. Abuelmakarem and Sarah Mohamed and Philippe Fournier-Viger and Md. Zaved H. Khan},
year = {2027},
journal = {Computational Biology and Chemistry},
doi = {10.1016/j.compbiolchem.2026.109431},
url = {https://doi.org/10.1016/j.compbiolchem.2026.109431}
}RIS
TY - JOUR TI - xLSTM-based deep learning model for predicting anticancer Activity of pyrano[3,2-a]phenazine hybrid derivatives: Proof of concept AU - Mohamed Aly Saad Aly AU - Aya I. Maiyza AU - Hala S. Abuelmakarem AU - Sarah Mohamed AU - Philippe Fournier-Viger AU - Md. Zaved H. Khan PY - 2027 JO - Computational Biology and Chemistry DO - 10.1016/j.compbiolchem.2026.109431 UR - https://doi.org/10.1016/j.compbiolchem.2026.109431 ER -
APA
Aly, M. A. S., Maiyza, A. I., Abuelmakarem, H. S., Mohamed, S., Fournier-Viger, P., & Khan, M. Z. H. (2027). xLSTM-based deep learning model for predicting anticancer Activity of pyrano[3,2-a]phenazine hybrid derivatives: Proof of concept. Computational Biology and Chemistry. https://doi.org/10.1016/j.compbiolchem.2026.109431
Source records
- crossref · retrieved 2026-09-25T20:27:01.520Z