Recent advances of sequencing technologies and collaborative projects enable providing high-throughput multi-level omics data from genomic level to metabolomic level. This type of data cannot be handled manually due to the mechanism is complex, and scale of the omics data is quite large and still growing. Therefore, the computational approach has been indispensable for the analysis of the data. Integrative network analysis is widely used to integrate the multi-level omics data in bioinformatics fields, and the analysis helps to understand the biological system. In the previous studies, several computational methods of interaction network construction have been proposed. However, most of the studies focused only on the strength of the interaction between arbitrary two features to construct the network. Thus, those methods cannot reflect the association between the interaction and clinical outcome. This thesis presents a simple but powerful method to construct an integrative network from multiple omics level. The connected gene pairs in the network are associated with the clinical outcome, and these associations are detected by the extended mutual information measure. Also, results of the thesis show that the network-based approach could provide a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of not only cancer patients but also other diseases.
