Jeff S. Piotrowski

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.

This study identifies resistance mechanisms in soybean against Sclerotinia sclerotiorum using multi-omics approaches, showing the role of jasmonate signaling, ROS scavenging, and phenylpropanoid pathway reprogramming in antifungal defense.

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.