LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug-target interaction prediction under multi-scenario cold-start settings.
- DOI
- 10.3389/fbinf.2026.1882476
- Published
- 2026
- Container
- Frontiers in bioinformatics
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3389/fbinf.2026.1882476,
title = {LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug-target interaction prediction under multi-scenario cold-start settings.},
author = {Guo S and Liu W and Zou J and Ban T and Dong G},
year = {2026},
journal = {Frontiers in bioinformatics},
doi = {10.3389/fbinf.2026.1882476},
url = {https://doi.org/10.3389/fbinf.2026.1882476}
}RIS
TY - JOUR TI - LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug-target interaction prediction under multi-scenario cold-start settings. AU - Guo S AU - Liu W AU - Zou J AU - Ban T AU - Dong G PY - 2026 JO - Frontiers in bioinformatics DO - 10.3389/fbinf.2026.1882476 UR - https://doi.org/10.3389/fbinf.2026.1882476 ER -
APA
S, G., W, L., J, Z., T, B., & G, D. (2026). LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug-target interaction prediction under multi-scenario cold-start settings.. Frontiers in bioinformatics. https://doi.org/10.3389/fbinf.2026.1882476
Source records
- pubmed · retrieved 2026-09-25T14:00:50.245Z