Published work

Defining subpopulations of differential drug response to reveal novel target populations

Comparisons of drugs have predominantly focused on identifying how individuals respond similarly, and there is a lack of data-driven insight into subpopulations of individuals with differential drug response.

We applied a novel machine learning algorithm, SEgmentation And Biomarker Enrichment of Differential treatment response (SEABED), to compare drug response between cancer drug pairs. This data-driven approach paves a new way for patient stratification to identify novel cancer subpopulations, their genetic biomarkers, and effective drug combinations.

Read our manuscript on npj Systems Biology & Applications.

SEABED code repository
Supplementary Website

Looking beyond the hype: Applied AI and machine learning in translational medicine

Review article on common applied AI and machine learning approaches used in translational medicine, particularly in the areas of drug discovery, imaging, and genomic medicine. We also discuss limitations of these technologies in our review article as they do not come without their limitations and shortcomings.

Read our review on EBioMedicine.