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Nature Methods
Bioinformatics AI
Yingying Sun, Jun A, Zhiwei Liu, Rui Sun, Liujia Qian, Samuel H. Payne, Wout Bittremieux, Markus Ralser, Chen Li, Yi Chen, Zhen Dong, Yasset Perez-Riverol, Asif Khan, Chris Sander, Ruedi Aebersold, Juan Antonio Vizcaíno, Jonathan R. Krieger, Jianhua Yao, Wen Han, Linfeng Zhang, Yunping Zhu, Yue Xuan, Benjamin Boyang Sun, Liang Qiao, Henning Hermjakob, Haixu Tang, Huanhuan Gao, Yamin Deng, Qing Zhong, Cheng Chang, Nuno Bandeira, Ming Li, Weinan E, Siqi Sun, Yuedong Yang, Gilbert S. Omenn, Yue Zhang, Ping Xu, Yan Fu, Xiaowen Liu, Christopher M. Overall, Yu Wang, Eric W. Deutsch, Luonan Chen, Jürgen Cox, Vadim Demichev, Fuchu He, Jiaxing Huang, Huilin Jin, Chao Liu, Nan Li, Zhongzhi Luan, Jiangning Song, Kaicheng Yu, Wanggen Wan, Tai Wang, Kang Zhang, Le Zhang, Peter A. Bell, Matthias Mann, Bing Zhang, Tiannan Guo
2026-07-28
TL;DR - This Nature Methods Perspective identifies mass spectrometry-based proteomics research areas where AI could enable major advances, spanning protein identification through virtual-cell modeling.
- Focuses on AI applications in mass spectrometry-based proteomics.
- Covers opportunities from protein identification to broader virtual-cell models.
- Presents a forward-looking research perspective rather than new experimental results.
- The brief provided does not specify particular methods, benchmarks, or findings.
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