The presentation provide an overview of cancer network analysis, which involves studying the relationships between entities in biological networks to understand cancer mechanisms. It covers computational backgrounds, graph types (simple, directed, bipartite), network analysis methods (community detection, link prediction, centrality computation, network comparison), applications in cancer (identifying hub genes, differential analysis, relation prediction), and related topics like network pharmacology. The slides highlight the importance of network analysis in revealing insights into cancer, such as key genes or proteins, functional modules, biomarkers, and potential drug targets. It also discusses tools like Cytoscape and WGCNA for network analysis and prediction tasks like identifying SNP-cancer relationships. Overall, the presentation emphasize the significance of complex network analysis grounded in graph theory for understanding intricate relationships and uncovering patterns in cancer research.
