We are a computational group embedded in a clinical setting. We focus on developing and applying statistical and machine learning tools for mining complex biological and clinical data, in order to identify cancer biomarkers and drug targets. We work closely with physicians and clinician scientists to translate our search outputs into better patient care.
We are part of the SMART-CARE consortium, which aims to develop novel systems medicine approaches to battle cancer recurrence, using the integration of high-quality proteome and metabolome mass spectrometry data with other ‘omics and clinical data. The role of our group in the consortium is providing innovative and robust computational solutions for mining and integrating mass-spectrometry data, in order to discover cancer biomarkers. Read more about SMART-CARE.
Functional Precision medicine
Functional precision medicine is the approach that directly uses perturbation assays on primary patient samples to guide clinical decisions. We develop computational tools, in the forms of R/Bioconductor packages and Shiny apps, for streamlined processing, analyzing, and interpreting the results from functional assays, such as ex-vivo drug sensitivity assays, in order to increase their sensitivity and robustness, which are critical for clinical applications. Read a relevant study.
Multi-Omics & Machine Learning
Multi-omics integration is a powerful tool for understanding complex disease mechanisms. It also has unique advantages in clinical biomarker identification as it enables us to distinguish biological signals from incidental variation due to measurement noise or confounding experimental factors that tend to affect only individual data sources. We apply machine learning tools, such as multi-table factor analysis, for better integration and interpretation of multi-omics datasets. Read a relevant study.
Research Group Leader
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