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Decoding Complexity: Multi-Partite Network Analysis for Environmental and Genetic Associations

Abstract Number:

2018 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Mira Park (1), Ji-Eun Shin (2)

Institutions:

(1) Eulji University, N/A, (2) Konyang University, N/A

Co-Author:

Ji-Eun Shin  
Konyang University

First Author:

Mira Park  
Eulji University

Presenting Author:

Mira Park  
Eulji University

Abstract Text:

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.

Keywords:

Multi-Partite Network|projection|genomic data|environmental factor| |

Sponsors:

Korean International Statistical Society

Tracks:

Miscellaneous

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