Author

Chuan‐Qi Zhong

Xiamen UniversityORCID 0000-0002-8354-7727Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Microbiology· 1

Signature Assays, Organisms & Methods

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Assay:proteomics· 3 Approach:deep learning· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Organism:multiple species· 1

Frequent Co-Authors

Jiahuai HaniD· 2 shared Jianwei ShuaiiD· 2 shared Mingxuan GaoiD· 2 shared Qingzu He· 2 shared Rongshan YuiD· 2 shared Chenxin LiiD· 1 shared Donghui GuoiD· 1 shared Fangfei ZhangiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersBioinformatics

Jiahuai HanORCID

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3 papers

Recent·Most cited

MicrobiologyNature CommunicationsPeer-reviewedMar 6, 2025

Elevated NAD+ drives Sir2A-mediated GCβ deacetylation and OES localization for Plasmodium ookinete gliding and mosquito infection

Yang Shi, Lin Wan, Mengmeng Jiao…·Jing Yuan

1 citation

plant (other)proteomics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintAug 27, 2022

Dear-DIAXMBD: deep autoencoder for data-independent acquisition proteomics

Qingzu He, Chuan‐Qi Zhong, Xiang Li…·Jiahuai Han

Dear-DIAXMBD is a deep autoencoder method for analyzing DIA proteomics data, achieving superior performance across different species and instrument platforms.

2 citations

multiple speciesproteomics

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BioinformaticsCommunications BiologyPeer-reviewedOct 14, 2021

DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

Mingxuan Gao, Wenxian Yang, Chenxin Li…·Jiahuai Han

DreamDIA-XMBD improves DIA proteomics analysis by using deep representation features for better identification and quantification than existing methods.

28 citations

proteomicsdeep learning

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