Hiroyuki Osada

Population-scale chemical response revealed by a barcoded yeast collection

The paper develops a barcoded yeast collection to map genotype-phenotype relationships across genetic diversity, revealing genetic variants linked to chemical responses.

BIONIC: Biological Network Integration using Convolutions

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

Identification of a novel family of benzimidazole species-selective Complex I inhibitors as potential anthelmintics

The paper identifies a novel family of benzimidazole compounds that selectively inhibit Complex I in helminths, potentially serving as new anthelmintics.

Improving prediction of compound function from chemical structure using chemical-genetic networks

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

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interaction networks

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

MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action

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.

Functional Annotation of Chemical Libraries across Diverse Biological Processes

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.