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Integration of data from omic studies with the literature-based discovery towards identification of novel treatments for neovascularization in diabetic retinopathy.
Diabetic retinopathy (DR) is a secondary complication of diabetes associated with retinal neovascularization and represents the leading cause of blindness in the adult population in the developed world. Despite research efforts, the nature of pathogenetic processes leading to DR is still unknown, making development of novel effective treatments difficult. Advances in omic technologies now offer unprecedented insight into global molecular alterations in DR, but identification of novel treatments based on massive amounts of data generated in omic studies still represents a considerable challenge. For this reason, we attempted to facilitate discovery of novel treatments for DR by complementing the interpretation of omic results using the vast body of information existing in the published literature with the literature-based discovery (LBD) approaches. To achieve this, we collected data from transcriptomic studies performed on retinal tissue from animal models of DR, performed a meta-analysis of these datasets and identified altered genes and pathways. Using the SemBT LBD framework, we have determined which therapies could regulate perturbed pathways or that could stabilize the gene expression alterations in DR. We show that by using this approach, we not only could reidentify drugs currently in use or in clinical trials, but also could indicate novel treatment directions for ameliorating neovascularization processes in DR.