Author

Zhenjiang Zech Xu

Nanchang UniversityORCID 0000-0003-1080-024XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:database / resource· 2 Research Focus:gene regulatory network inference· 2 Assay:metagenomics· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Approach:deep learning· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Hongli MaiD· 2 shared Yunfan JiniD· 2 shared Zhi John LuiD· 2 shared Chenhao LiiD· 1 shared Fei Gan· 1 shared Han HuiD· 1 shared Jianwei MaiD· 1 shared Jiyun CuiiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBioinformatics

Hongli MaORCID

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3 papers

Recent·Most cited

Conserved 3' Stem-Loop Structures Enable Comprehensive Analysis of Bacterial Transcription Termination in Metagenomes

Yunfan Jin, Jiyun Cui, Rulong Liu, Hongli Ma, Xiaomin Xu, Shufang Wu, Fei Gan, Zhi John Lu, Zhenjiang Zech Xu

BATTER predicts bacterial transcript 3' ends by analyzing conserved stem-loop structures, revealing clade-specific properties and premature termination in AMR genes.

Microbiome · Jul 6, 2026Peer-reviewedBioinformatics

RNA-ligand interaction scoring via data perturbation and augmentation modeling

Hongli Ma, Letian Gao, Yunfan Jin, Jianwei Ma, Yilan Bai, Xiaofan Liu, Pengfei Bao, Kebin Liu, Zhenjiang Zech Xu, Zhi John Lu

RNAsmol is a sequence-based deep learning framework that predicts RNA-small molecule interactions using data perturbation and augmentation, achieving improved performance over existing methods.

Nature Computational Science · Jun 24, 2025Peer-reviewedBioinformatics

StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data

Han Hu, Yuxiang Tan, Chenhao Li, Junyu Chen, Yan Kou, Zhenjiang Zech Xu, Yang‐Yu Liu, Yan Qin Tan, Lei Dai

StrainPanDA links strain composition and gene content in metagenomic data to understand microbial functions and phenotypes.

iMeta · Aug 1, 2022Peer-reviewedBioinformatics