Article
Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis.
Nature communications - 14 Mar 2025
Zheng Yongqiang, Yu Kai, Lin Jin-Fei, Liang Zhuoran, Zhang Qingfeng, Li Junteng, Wu Qi-Nian, He Cai-Yun, Lin Mei, Zhao Qi, Zuo Zhi-Xiang, Ju Huai-Qiang, Xu Rui-Hua, Liu Ze-Xian
Abstract excerpt
Genetic variants can affect protein function by driving aberrant subcellular localization. However, comprehensive analysis of how mutations promote tumor progression by influencing nuclear localization is currently lacking. Here, we systematically characterize potential shuttling-attacking mutations (SAMs) across cancers through developing the deep learning model pSAM for the ab initio decoding of the sequence...
Read the complete abstract on PubMedTopics
- Deep Learning
- Humans
- Neoplasms
- Mutation
- Carcinogenesis
- Cell Nucleus