Author

Zeming Li

Hong Kong Baptist UniversityORCID 0009-0002-0895-4354Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:large language model· 1 Approach:machine learning (classical)· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Lu ZhangiD· 4 shared Aiping LüiD· 2 shared Chonghao WangiD· 2 shared Yu Gong XuiD· 2 shared ADVANCED Working Group· 1 shared Bingyi WangiD· 1 shared Di Zhu· 1 shared Dongrui Peng· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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4 shared papersBioinformatics

Lu ZhangORCID

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4 papers

Recent·Most cited

Illuminating phage dark matter beyond the reach of structural alignments with PhageFlare

Chonghao Wang, Yu Gong Xu, Zeming Li, Louxin Zhang, Lu Zhang

PhageFlare predicts functions for phage proteins with no structural matches, outperforming baselines and annotating many previously unresolved proteins.

bioRxiv · Oct 29, 2026 · PreprintMicrobiology

RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels

Zeming Li, Xufei Luo, Zhenhua Yang, Huayu Zhang, Bingyi Wang, Long Ge, Zhaoxiang Bian, James Zou, Yaolong Chen, Lu Zhang, ADVANCED Working Group, Ye Wang, Haodong Li, Di Zhu, Yuanyuan Yao, , Shilin Tang

RAPID is an automated tool using large language models to generate medical reporting checklists, achieving high accuracy on CONSORT and CONSORT-AI datasets.

Journal of the American Medical Informatics Association · May 27, 2025Peer-reviewedBioinformatics

Bio-Mol:Pretraining Multimodality Bioactivity Profile for Enhancing Small Molecule Property Prediction

Hiu Fung Yip, Xiao Wei, Zeming Li, Qing Ren, Dongsheng Cao, Lu Zhang, Aiping Lü

Bio-Mol improves small molecule property prediction by integrating missing bioactivity profiles through pretraining and finetuning, achieving a 5.2% higher AUROC than previous models.

bioRxiv · Nov 2, 2023 · PreprintBioinformatics

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

Yu Gong Xu, Chonghao Wang, Zeming Li, Yunpeng Cai, Ouzhou Young, Aiping Lü, Lu Zhang

This paper introduces PRSIMD, a machine learning model that integrates genetic and non-genetic factors to improve disease risk prediction, showing better performance than existing PRS methods.

IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · Dec 6, 2022Peer-reviewedBioinformatics