Chronic kidney disease has become a serious public health issue. There are currently over one million patients receiving renal replacement therapy worldwide. Progression of CKD is associated with a number of serious complications including increased incidence of cardiovascular disease, hyperlipidemia, anemia and metabolic bone disease. Moreover, metabolic disorders induced nephropathy is also a significant cause of chronic kidney disease and end-stage renal failure.
Systems biology aims at achieving a system-level understanding of living organisms and applying the obtained knowledge to various fields including medicine. To make the best use of biological databases and the knowledge they contain, different kinds of information from different sources must be integrated in ways that make sense to biologists. In this context, text mining technologies can be directly applied for systems level understanding that have been substantially improved over the past few years.
Hence, we created a database that contains possible information for disease-gene association in various contexts. Renal disease associated literature were text mined and “Omics” information also included to construct a database entitled as Renalomics. We believe, this database would serve to the steadily increasing impact of omics data sources on renal systems biology in future.