PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries

Authors

Lizhuang Liu (ORCID: https://orcid.org/0000-0001-8032-4598)
Lin Lv (ORCID: https://orcid.org/0000-0002-5438-1350)
Xinpei Yi (ORCID: https://orcid.org/0000-0001-8562-1655)
Ben Wang (ORCID: https://orcid.org/0000-0002-4134-1835)
Junhua Zhang (ORCID: https://orcid.org/0000-0002-8936-3822)
Chaoyin She
Zhihao Yan
Hongwen Zhu
Yan Fu
Hongwei Zhao
Zhiyuan Cheng

Institutions

Chinese Academy of Sciences (CN)
Fudan University (CN)
Guangzhou University (CN)
Shanghai Advanced Research Institute (CN)
Academy of Mathematics and Systems Science (CN)
Ministry of Education (ET)
University of Chinese Academy of Sciences (CN)

Publication Details

Journal: Advanced Science
Published: 2026-05-27
DOI: https://doi.org/10.1002/advs.75856
Primary Topic: Advanced Proteomics Techniques and Applications
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities

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article

PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries

Lizhuang Liu, Lin Lv, Xinpei Yi, Ben Wang et al.

May 27, 2026

Advanced Science

Advanced Proteomics Techniques and Applications

article

PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries

Lizhuang Liu, Lin Lv, Xinpei Yi, Ben Wang, Junhua Zhang, Chaoyin She, Zhihao Yan, Hongwen Zhu, Yan Fu, Hongwei Zhao, Zhiyuan Cheng

May 27, 2026article en

Abstract

No abstract available for this paper.

Advanced Science

Chinese Academy of Sciences (CN), Fudan University (CN), Guangzhou University (CN), Shanghai Advanced Research Institute (CN), Academy of Mathematics and Systems Science (CN), Ministry of Education (ET), University of Chinese Academy of Sciences (CN)

National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Openalex Percentile: Top 11%

Advanced Proteomics Techniques and Applications

DOI Open Access PDF OpenAlex

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