Mami Yoshimura

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.

Overexpression profiling reveals cellular requirements in context of genetic backgrounds and environments

The paper identifies genes whose overexpression is adaptive under stress in budding yeast, revealing cellular requirements influenced by genetic background and environment.

BIONIC: Biological Network Integration using Convolutions

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

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.

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.