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Gene. 2016 Sep 15;590(1):68-78. doi: 10.1016/j.gene.2016.05.044. Epub 2016 Jun 2.

Network analysis of genes and their association with diseases.

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Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece.
Lawrence Berkeley Lab, Joint Genome Institute, United States Department of Energy, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA.
Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece. Electronic address:


A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms.


Drug design; Gene-disease associations; Human disease network; Human disease-related genes network

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