Author

Zhangyuan Lin

National University of SingaporeORCID 0009-0007-9631-5646Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Synthetic Biology· 2

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 1 Research Focus:database / resource· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1 Assay:proteomics· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Chueh Loo PohiD· 2 shared Dan ZhengiD· 1 shared Jan Ron Goh· 1 shared Jing Wui YeohiD· 1 shared Jingyun ZhangiD· 1 shared Louise F. H. FunkeiD· 1 shared Sangeetha Shanmugam· 1 shared Wenjun LiaoiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 21 mutual co-author links

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2 shared papersSynthetic Biology

Chueh Loo PohORCID

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2 papers

Recent·Most cited

Synthetic BiologybioRxivPreprintOct 9, 2026

High-throughput in vivo DNA data encoding by multiplexed conjugationand orthogonal base editing

Louise F. H. Funke, Zhangyuan Lin, Wenjun Liao…·Chueh Loo Poh

The paper introduces a system for high-throughput in vivo DNA data encoding using bacterial conjugation and base editing, successfully encoding and retrieving binary data with high accuracy.

DOI 10.64898/2026.10.08.757515

bacteria (other)DNA methylation

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Synthetic BiologybioRxivPreprintMar 30, 2025

Machine learning-assisted enzyme engineering through ultra-high throughput sorting and large-scale sequence-function data generation

Jingyun Zhang, Sangeetha Shanmugam, Jing Wui Yeoh…·Chueh Loo Poh

This paper introduces PUSDA, a framework combining ultra-high throughput sorting and NGS to generate large-scale sequence-function data, enabling ML-driven enzyme engineering with improved efficiency and productivity.

1 citation

proteomicsmachine learning (classical)

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