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Currently seeking Ph.D. students.
Our research uses computational approaches to understand the cancer genome. We develop new tools and algorithms for the integrative analysis of high throughput cancer genomic and proteomic datasets. Computational analysis has the capacity to screen the entire genome and epigenome to find events that may drive cancer progression. By integrating with other datasets such as drug responses, biomarkers can be identified to prioritize patients who may respond to the therapy while sparing patients who may not respond. Our group has extensive experience in data integration and cancer genomics.
2017, CPRIT Scholar in Cancer Research.
2019, UT STARs Award.
The research in my lab is focused on understanding adult and pediatric cancers through deep mining of genomic, transcriptomic, and proteomic data sets. Our broad aim is to (1) characterize genes and pathways underlying cancer initiation and progression (2) predict tumor responses to therapeutic interventions based on their genomic profiles (3) integrative analysis of proteomic and genomic datasets (4) develop new methods and pipelines to leverage massive datasets in the public domain and address relevant questions in the field.