PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context
Abstract Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional imp...
Saved in:
| Main Authors: | Guojie Zhong, Yige Zhao, Demi Zhuang, Wendy K. Chung, Yufeng Shen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62318-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A probabilistic graphical model for estimating selection coefficients of nonsynonymous variants from human population sequence data
by: Yige Zhao, et al.
Published: (2025-05-01) -
A framework for exhaustively mapping functional missense variants
by: Jochen Weile, et al.
Published: (2017-12-01) -
Missense variants in PRKCD: elucidating their potential association with breast cancer
by: Sameen Zafar, et al.
Published: (2025-07-01) -
IRAC's Insecticide Mode of Action Classification
by: Frederick M. Fishel
Published: (2005-12-01) -
IRAC's Insecticide Mode of Action Classification
by: Frederick M. Fishel
Published: (2011-06-01)