PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

Guojie Zhong; Yige Zhao; Demi Zhuang; Wendy K. Chung; Yufeng Shen

doi:10.1038/s41467-025-62318-4

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

G Guojie Zhong Y Yige Zhao D Demi Zhuang W Wendy K. Chung (Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School) Y Yufeng Shen (Department of Systems Biology, Columbia University Irving Medical Center)

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Article Details

Journal Nature Communications

Volume / Issue Vol. 16, Issue 1

Published August 05, 2025

DOI 10.1038/s41467-025-62318-4

ISSN 2041-1723

Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (5)

G

Guojie Zhong

Y

Yige Zhao

D

Demi Zhuang

W

Wendy K. Chung

Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School

Y

Yufeng Shen

Department of Systems Biology, Columbia University Irving Medical Center

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

Article Details

Journal Info

Authors (5)

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