Enhancing Disease Understanding and Clinical Outcomes through the Integration of Large-Scale Omics Datasets Using Systems Biology
Dr. Gürkan Bebek, Case Western Reserve University
Abstract: Network analysis is revolutionizing our understanding of cancer, offering insights into its complex mechanisms and paving the way for personalized medicine. We will explore this multifaceted approach, beginning with its ability to decipher the functional consequences of specific mutations. By analyzing interactions within biological networks, we can discover how mutations, such as those in the APC gene in colorectal cancer, trigger cascading effects and interfere with cellular pathways. This understanding of dysregulated pathways forms the foundation for patient stratification. We can group individuals based on their unique pathway disruptions by analyzing network alterations in cancer patients. Features discovered by frequent subgraph mining offer insights into the underlying disease mechanisms. This approach holds potential for both prognosis prediction and the development of tailored treatment strategies. For example, we will explain discovering distinct patient groups in low-grade glioma using our unsupervised bottom-up approach. Specific subnetwork alterations both validate our approach and reveal previously unknown subgroups with distinct clinical needs. This exploration of network analysis in cancer research highlights its transformative power in unraveling the complexities of this disease and paving the way for more targeted therapies.
Biography: Dr. Gurkan Bebek, a computer scientist specializing in complex diseases such as Cancer and Alzheimer’s, holds a Ph.D. in Computer Science and an M.S. in Clinical Investigation from Case Western Reserve University (CWRU). His research centers on systems biology tools, seamlessly integrating diverse high-throughput -omics data. Dr. Bebek has authored over 35 papers in bioinformatics, garnering recognition and funding from NIH, Velasono, and CWRU CTSA. As an Assistant Professor at the School of Medicine, he continues to lead innovative research at the intersection of computer science and biomedical sciences, advancing our understanding of complex diseases. Dr. Bebek’s lab specializes in computational biology and bioinformatics, with a mission to identify key regulatory networks and potential therapeutic targets through large-scale biological data analysis. The overarching goal is to uncover patterns within this data that can improve outcomes. Recently, his team explored potential biomarkers for early-stage Alzheimer’s disease and lower-grade gliomas using machine learning algorithms, with promising implications for early diagnosis and personalized treatment. Dr. Bebek collaborates closely with clinical researchers and basic scientists, bridging the gap between fundamental science and practical clinical applications.
DATE: July 22, Monday @ 10:00
Place: EA 409