Precision medicine is empowered by identifying robust drug response biomarkers. However, experimental noise in drug high-throughput screens can lead to biased estimates of biomarker-drug response associations.
Here, we introduce a novel, flexible dose-response fitting algorithm based on Gaussian processes to produce confidence intervals for drug response. In addition to this new dose-response metric, we provide a Bayesian framework which incorporates the uncertainty estimates for the identification of biomarkers. This suggests the importance of quantifying uncertainty for in vitro drug experiments and advances the development of precision medicine by improving reproducibility of biomarker association studies.
Read our manuscript on eLife.GP drug response fitting code repository
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
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.