Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics