Author

Xiaoming Zhang

ORCID 0000-0001-9430-5945Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Xiaoming Zhang4 papers · Cleveland Clinic

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:drug response / drug discovery· 2 Assay:protein sequence· 2 Research Focus:protein structure prediction· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Assay:DNA methylation· 1

Frequent Co-Authors

Linlin Chao· 2 shared Qirong YangiD· 2 shared Yucheng GuoiD· 2 shared Ziyao Xu· 2 shared Bozhen HuiD· 1 shared Chun Shang· 1 shared Dongli Lu· 1 shared Feiran LiiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Linlin Chao

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintSep 9, 2026

ProteinSage: From implicit learning to explicit structural constraints for efficient protein language modeling

Lingdong Shen, Linlin Chao, Tang Liu…·jinren Ni

ProteinSage uses structural constraints during pretraining to improve protein language modeling with less data, achieving competitive performance on structure-aware tasks.

DOI 10.64898/2026.03.17.712034

protein sequencedeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintDec 29, 2025

MicroGenomer: A Foundation Model for Transferable Microbial Genome Representations Enabling Multi-scale Genomic Understanding and Ecophysiological Trait Prediction

Qiang Kang, Yucheng Guo, Bozhen Hu…·Peng Yin

DOI 10.64898/2025.12.28.696777

bacteria (other)whole-genome sequencing

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BioinformaticsbioRxivPreprintSep 3, 2025

BioLab: End-to-End Autonomous Life Sciences Research with Multi-Agents System Integrating Biological Foundation Models

Ruofan Jin, Yucheng Guo, Yuanhao Qu…·Le Cong

BioLab uses a multi-agent system with foundation models to autonomously design and validate antibodies, achieving superior performance compared to existing methods.

3 citations

humanprotein sequence

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