Cancer AI Conversations: Knowledge Graphs in the AI Data Ecosystem
To Know
About this Class
This session of the Cancer AI Conversations is an opportunity to highlight how knowledge graphs are advancing AI for biomedical research from multiple perspectives.
Dr. Haitham Elmarakeby has expertise in machine learning and data mining with a special interest in applying cutting-edge computational techniques to better understand progression and drug resistance in cancer. His machine learning models integrate multiple data modalities such as gene expression, mutations, copy number variations, and methylations to accurately predict outcomes in real patients and cell lines models.
Dr. Benjamin Gyori’s research combines computational modeling, machine learning, natural language processing, and human–machine interaction to improve our understanding of complex human biology, opening doors to advances in healthcare. His interest in the interdisciplinary field of computational systems biology stems from his fascination with mathematical and computational models of natural systems.
Dr. Jonathan Silverstein is internationally known for his expertise and research in the application of advanced computing architectures to biomedicine. Dr. Silverstein’s research interests include clinical informatics, imaging/visualization/virtual reality, vocabularies, virtual organizations, learning health systems and oncology informatics.