Wednesday, Aug 7: 9:00 AM - 9:05 AM
2018
Contributed Speed
Oregon Convention Center
Unraveling intricate relationships between diseases and genes poses challenges, demanding intuitive representation through smart visualization. Network analysis has gained prominence as a solution. While one-mode network analysis is common, it often falls short in identifying comprehensive information, such as gene-disease pairs or genes linked to the same environmental factors. In this study, we adopt multi-partite network analysis. A distinctive feature is the network's composition of mutually exclusive sets of nodes, with edges connecting nodes across different sets. Compressed relationships within sets are also explored through multi-level projections. We propose two types of projections for obtaining unipartite projections: sequential projection and concurrent projection. Applying this methodology to the Korean Association Resource (KARE) project, featuring 327,872 SNPs across 8,840 individuals, we considered three distinct datasets: genetic factors, environmental factors, and Metabolic Syndrome components. The resulting multi-partite network and projected lower mode network provided valuable insights into direct and indirect relationships.
Multi-Partite Network
projection
genomic data
environmental factor
Main Sponsor
Korean International Statistical Society