GeoSeq-EGNN: A geometry-sequence fused equivariant graph neural network for predicting short linear motifs within intrinsically disordered regions
- DOI
- 10.1016/j.jmgm.2026.109610
- Published
- 2027-01
- Container
- Journal of Molecular Graphics and Modelling
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jmgm.2026.109610,
title = {GeoSeq-EGNN: A geometry-sequence fused equivariant graph neural network for predicting short linear motifs within intrinsically disordered regions},
author = {Jun Zhang and Jinming Zhang and Liting Zheng and Huiqiong Ruan and Yuxiang Li and Yi Li and Guilan Luo},
year = {2027},
journal = {Journal of Molecular Graphics and Modelling},
doi = {10.1016/j.jmgm.2026.109610},
url = {https://doi.org/10.1016/j.jmgm.2026.109610}
}RIS
TY - JOUR TI - GeoSeq-EGNN: A geometry-sequence fused equivariant graph neural network for predicting short linear motifs within intrinsically disordered regions AU - Jun Zhang AU - Jinming Zhang AU - Liting Zheng AU - Huiqiong Ruan AU - Yuxiang Li AU - Yi Li AU - Guilan Luo PY - 2027 JO - Journal of Molecular Graphics and Modelling DO - 10.1016/j.jmgm.2026.109610 UR - https://doi.org/10.1016/j.jmgm.2026.109610 ER -
APA
Zhang, J., Zhang, J., Zheng, L., Ruan, H., Li, Y., Li, Y., & Luo, G. (2027). GeoSeq-EGNN: A geometry-sequence fused equivariant graph neural network for predicting short linear motifs within intrinsically disordered regions. Journal of Molecular Graphics and Modelling. https://doi.org/10.1016/j.jmgm.2026.109610
Source records
- crossref · retrieved 2026-11-10T12:43:07.147Z