Research Areas
- Bioinformatics
- Systems biology
- Network analysis
- Machine learning
- Link prediction
Scientific Achievements
- Developed a novel bioinformatics pipeline and algorithm to identify potential new liver cancer drug targets by analyzing complex biological data
- Established a collaboration with the Department of Human Genetics to support experimental validation of promising research findings
- Selected for an oral presentation and multiple poster presentations at the Intelligent Systems for Molecular Biology (ISMB) Conference (July 2026)
- Presented research findings at national and international scientific meetings, including the International Conference on Cancer Health Disparities and the UTRGV School of Medicine Research Symposium (February 2026)
Funding
RCMI Funding
- U54MD019970, NIH/NIMHD: Pilot Project Principal Investigator, “Integrative Predictive Approach to Reveal Understudied Kinases in Liver Cancer”
Scientific Advance
Integrative Predictive Approach to Reveal Understudies Kinases in Liver Cancer
The Liver cancer is one of the leading causes of cancer-related deaths worldwide, and many of the biological processes that drive its development remain poorly understood. This project is developing a new computational approach to identify important proteins that control cell behavior in the liver and may contribute to cancer progression. By analyzing large amounts of complex biological data, the project aims to uncover previously overlooked targets that could improve our understanding of liver cancer and guide future treatment strategies. The findings may help researchers discover new biomarkers and therapeutic targets for patients with liver cancer.
NIH/NIMHD #U54MD019970
