Research has suggested that peroxisome proliferator-activated receptors (PPARs) play roles in many diseases and have been indicated as treatment targets for multiple diseases. For example, PPARA has been suggested as a therapeutic target for chronic lymphocytic leukemia (CLL) by several studies. Thus, in drug development for CLL, PPARG could be a priority candidate for testing. However, PPARG is not the only potential treatment target for CLL. There have been more than 70 genes proposed to play promotion roles in the development of CLL and thus are potential treatment targets for such diseases. Often, the treatment targets of a disease are functionally associated with each other, forming a complex network that play an integrated function in influencing the pathological development of that specific disease. Identifying the connection between PPARs and the other vertex within the network, and comparing and weighting the significance of PPARs with other nodes may offer new insights for understanding the roles of PPARs in the disease.

This Special Issue aims to collate original research and review papers that integrate existing databases and knowledge-based algorithms into experimental and computational studies to explore the connections and weights of PPARs with their parallel competitors in the pathological development of diseases.

Potential topics include but are not limited to the following:

• Construct experiment-data-based networks to explore the significance of PPARs in disease
• Construct knowledge database-based networks to explore the importance of PPARs in disease
• In-silico modeling integrates different data to explore the significance of PPARs in disease
• Application of computational algorithms to explore the significance of PPARs in disease using graph theory
• Development of a computational algorithm to explore the significance of PPARs in disease using graph theory
• Co-expression analysis of protein-protein interaction network to explore the significance of PPARs in disease
• Reviews for PPAR involved networks and the weight of PPARs within networks/pathways
• Reviews for PPAR involved pathways and the weight of PPARs within pathways
• Clinical data-based study between PPARs and relevant clinical parameters for their importance in disease
• Meta-analysis exploring the weight of PPARs in relevant disease