Investigators

Summary

Our group's primary focus is on developing integrative machine learning approaches for extracting therapeutic and biological insights from highly heterogeneous omic datasets, clinical and drug response data, with the purpose of advancing precision medicine. Our projects span across the following areas: 

• Cancer Genomics: Leveraging machine learning techniques to analyze genomic data from cancer patients, identifying genetic mutations, biomarkers, and potential therapeutic targets for personalized cancer treatment.
• Single Cell and Spatial Omics: Developing statistical methods for single-cell multi-omics integration, integrating data from different omic layers (such as genomics, proteomics, transcriptomics, epigenomics) to gain a comprehensive understanding of cellular processes and disease mechanisms.
• Epigenetics: Studying how epigenetic modifications influence an individual's response to different drugs, integrating epigenetic data with clinical information to develop predictive models for personalized drug selection.

Our projects are executed through multi-disciplinary collaborations, recognizing that precision medicine requires expertise from various domains. By leveraging machine learning and integrating diverse datasets, our aim is to contribute to the advancement of precision medicine, ultimately leading to more targeted and effective treatments for patients.