Shuyi Ma

MetworkPy provides a Python package for objective-free graph- and information-theoretic analysis of genome-scale metabolic models to identify metabolic features perturbed by transcription factors.

This study shows that ferroptosis, a form of cell death, is enhanced by a virulent Mtb strain, SA161, and inhibiting it reduces bacterial spread in mice.

This paper introduces Transcript-Capture Seq to enrich bacterial mRNA from host samples, enabling analysis of the complete Mtb transcriptome in clinical samples and revealing gene expression patterns associated with in vivo bacterial activity.

The paper predicts Mtb fitness using a transcription factor activity-based machine learning model trained on RNA-seq data and regulatory network analysis.

The paper develops a computational model to identify synergistic anti-TB drug combinations using transcriptomic data and validates its predictions with clinical trial data.

The paper identifies novel drug potentiators in Mycobacterium tuberculosis using a network-based genetic screening approach to uncover stress response adaptations to isoniazid.

The paper introduces IDREAM, a method that combines gene regulatory and metabolic networks to improve phenotype prediction in yeast, showing better accuracy than existing methods.