Videos

Speaker: • Kasthuri Kannan, Associate Professor, University of Texas MD Anderson Cancer Center Session type: Full Length Session Abstract: For decades, relational databases and static networks have been the mainstream for data-driven biology and medicine. Despite the simplicity of these approaches, these static architectures do not enable unbiased hypothesis generation and systems biology-based discovery. Systems biology attempts to capture the dynamic and integrative nature of biology. Graph databases as mutable and dynamic querying structures coalesce data through interactions, enabling the study of biological systems as complex adaptive systems and providing a platform for hypothesis generation. This article introduces a generalized patient-centric graph schema that integrates molecular and clinical datasets, providing an unbiased hypothesis-generation paradigm for cancer research. It also highlights an example of a database analysis using a commercial graph solution (Neo4j) that reveals the integrated landscape for a molecular subtype of brain tumors.