Author

Wei Zhang

Central South UniversityORCID 0000-0003-3094-9711Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Wei Zhang40 papers · Chinese Academy of Sciences

Biology Disciplines

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Biochemistry· 1 Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Abraham Ayodeji AdegboroiD· 1 shared Benpeng Zhao· 1 shared Chaoqun Dai· 1 shared Chen LiiD· 1 shared Christopher OetheimeriD· 1 shared Deborah Oluwatosin Fasoranti· 1 shared Elle Schnieder· 1 shared Guanyu YuiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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1 shared paperBioinformatics

Abraham Ayodeji AdegboroORCID

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3 papers

Recent·Most cited

BiochemistryNucleic Acids ResearchPeer-reviewedJun 22, 2026

Bottlebrush Polymer Conjugates for Enhanced Antisense Oligonucleotide Therapy in Myotonic Dystrophy Type 1

Yao Li, Christopher Oetheimer, Yuyan Wang…·Ke Zhang

This paper demonstrates that bottlebrush polymer conjugates enhance antisense oligonucleotide therapy for DM1, improving muscle distribution and splicing correction in a mouse model.

DOI 10.1093/nar/gkag619

mouseexperimental assay

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BioinformaticsComputational and Structural Biotechnology JournalPeer-reviewedJun 29, 2024

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

Hongwei Liu, Wei Zhang, Yihao Zhang…·Xuejun Li

The paper introduces Mime, an R package for building and visualizing machine learning models to predict clinical outcomes and select features using transcriptional data.

182 citationstop 1%

bulk RNA-seqmachine learning (classical)

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Systems BiologybioRxivPreprintAug 21, 2022

Increasing the sensitivity, recovery, and integrality of spatially resolved proteomics by LCM-MTA

Lei Gu, Xumiao Li, Ziyi Li…·Hui Wang

This paper introduces LCM-MTA, a method for spatially resolved proteomics of trace FFPE tissues, improving sensitivity, recovery, and integrality.

4 citations

humanproteomics

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