Author

Chencheng Xu

King Abdullah University of Science and TechnologyORCID 0000-0002-2262-6966Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Chencheng Xu2 papers · Ludwig Cancer Research

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 2 Assay:ChIP-seq· 1 Research Focus:clustering· 1 Approach:diffusion model· 1 Research Focus:dimensionality reduction· 1 Approach:large language model· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Bin ZhangiD· 2 shared Haoyang LiiD· 2 shared Juexiao ZhouiD· 2 shared Siyuan CheniD· 2 shared Di WangiD· 1 shared Guowei LiiD· 1 shared Liwei LiuiD· 1 shared Longxi ZhouiD· 1 shared

Activity by Year

2024-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersBioinformatics

Bin ZhangORCID

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2 papers

Recent·Most cited

An AI Agent for Fully Automated Multi-omic Analyses

Juexiao Zhou, Bin Zhang, Guowei Li, Xiuying Chen, Haoyang Li, Xiaopeng Xu, Siyuan Chen, Wenjia He, Chencheng Xu, Liwei Liu, Xin Gao

AutoBA is an AI agent that automates multi-omic analyses using large language models, offering adaptability and security across various sequencing and omics data types.

Advanced Science · Oct 3, 2024Peer-reviewedBioinformatics

PPML-Omics: a Privacy-Preserving federated Machine Learning method protects patients' privacy in omic data

Juexiao Zhou, Siyuan Chen, Yulian Wu, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Wei Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, Wenkai Han, Chencheng Xu, Di Wang, Xin Gao

PPML-Omics is a privacy-preserving federated learning method that balances privacy protection and model utility across three omic data tasks.

Science Advances · Jan 31, 2024Peer-reviewedBioinformatics