A survey on computational approaches to identifying disease biomarkers based on molecular networks

The disease biomarkers can help make accurate diagnosis and therefore give appropriate interventions. In the past years, the accumulation of various kinds of 'omics' data, e.g. genomics and transcriptomics, makes it possible to identify disease biomarkers in a more efficient way. In particular, the molecular networks that describe the functional relationships among molecules enable the identification of disease biomarkers from a systematic perspective. In this paper, we surveyed the recent progress on the computational approaches that have been developed to identify disease biomarkers based on molecular networks. In addition, we introduced the popular resources about human interactomes and regulatomes as well as human diseasomes, whose availability makes it possible to predict the disease biomarkers with the utility of networks.