Differential network as an indicator of osteoporosis with network entropy

Ma, Lili; Du, Hongmei; Chen, Guangdong

Recently genome-wide gene expression profiles and PPIs hold the major promise to uncover the biological progression, which are subsequently pivotal in getting across molecular mechanisms of diseases (26) . Based on the landscape, functional network entropy can be readily detected to facilitate the valuable application of PPIs on disease research. Previous studies have revealed that increased entropy appears to be a hallmark of cancer systems (27, 28) . Thus we utilized the previous hypothesis that the network entropy could discriminate the inflammatory network from the normal network (20) . The systematic calculation and comparison were performed to test whether the network entropy can also discriminate the low PBM network from high PBM network. By applying a significance test for the difference in global network entropy of low and high PBM networks, we found the global network entropy of PBM networks is significantly differential (P<0.05). The result claimed that the network entropy provided good discrimination between low and high PBM networks. Moreover, this method can serve as a significant foundation for further exploring the molecular mechanisms of other diseases and developing control strategies. One unique advantage of the present method compared with custom networks identifying analysis is that the entropy performs better than other topological metrics of the network in characterizing the inflammatory disease, which was detected by the Network Analyzer plug-in in Cytoscape (20, 29) . Thereby, the significantly different network we captured was more valuable and promising in understanding molecular mechanisms of diseases and reliable in therapeutic options.