Author

Jin Wu

Fudan UniversityORCID 0000-0001-9926-0993Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Cell Biology· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:experimental assay· 2 Organism:mouse· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:database / resource· 1 Research Focus:host-pathogen & immune response· 1 Approach:machine learning (classical)· 1 Research Focus:metabolism & bioenergetics· 1

Frequent Co-Authors

Feng‐Jung CheniD· 2 shared Hongyuan YangiD· 2 shared Mingming GaoiD· 2 shared Xiaoqin Wu· 2 shared Yang LiuiD· 2 shared Chengxin MaiD· 1 shared Chunmei ChangiD· 1 shared Daiwen CheniD· 1 shared

Activity by Year

2020-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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2 shared papersCell Biology

Feng‐Jung ChenORCID

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3 papers

Recent·Most cited

Cell BiologyHepatology CommunicationsPeer-reviewedJul 14, 2025

ORP2 regulates free cholesterol accumulation in hepatocytes during MASH

Jin Wu, Yudi Zhao, Liwen Qiu…·Hongyuan Yang

This study shows that ORP2 prevents free cholesterol accumulation in mouse liver, and its absence leads to severe MASH with cholesterol crystals.

2 citations

mouseexperimental assay

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Cell BiologyProceedings of the National Academy of SciencesPeer-reviewedJul 3, 2025

PI(4)P recruits CIDE proteins to promote the formation of unilocular lipid droplets during adipogenesis and hepatic steatosis

Jin Wu, Mingming Gao, Xiaoqin Wu…·Hongyuan Yang

The paper shows PI(4)P recruits CIDE proteins to form unilocular lipid droplets in adipogenesis and hepatic steatosis by interacting with ORP2/5 and PI4K2A.

5 citations

mouseproteomics

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BioinformaticsBriefings in BioinformaticsPeer-reviewedJul 6, 2020

An in silico approach to identification, categorization and prediction of nucleic acid binding proteins

Lei Xu, Shanshan Jiang, Jin Wu, Quan Zou

The paper builds a database of nucleic acid binding proteins and predicts their functions using machine learning with 77.43% accuracy.

106 citationstop 2%

protein sequencemachine learning (classical)

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