Jing Wui Yeoh

This paper evaluates LLM capabilities for analyzing genome-scale metabolic models, identifying strengths and limitations across domain knowledge, flux prediction, pathway construction, and optimization tasks.

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.

The paper develops a deep CNN-based system for automated microbial classification using colony images, achieving high accuracy in identifying five species.

The paper develops tunable thermal-repressible split-T7 RNA polymerase systems for dynamic gene regulation using temperature as a control cue.

BMSS2 is a Python package for model selection and identifiability analysis in synthetic biology, offering tools for simulation, parameter inference, and sensitivity analysis through a database-driven framework.