Sheena Claire Li

This study identifies a novel antifungal compound targeting ALS, validated through genome mining, biochemical assays, and a mouse model of invasive aspergillosis.

BIONIC integrates biological networks using deep learning to improve accuracy and scalability, identifying novel gene interactions in yeast.

The paper benchmarks structure descriptors and similarity coefficients using chemical-genetic interaction data to improve compound function prediction.

The paper develops CG-TARGET to predict compound biological processes by integrating chemical-genetic and genetic interaction networks, showing improved accuracy and FDR control.

The paper introduces MOSAIC, a database and web resource for chemical-genetic interaction data, enabling mode-of-action prediction and discovery of compound effects in yeast.

This study develops a yeast-based chemical-genetic screening system to annotate compound functionality across biological processes and identifies compounds with dual modes of action.

The paper introduces COMPRESS-GI, an algorithm that efficiently selects informative genes for genetic interaction screens, enabling large-scale chemical genetic screens with high throughput.