Abstract
Many human genetic disorders and diseases are known to be related to each other through frequently observed co-occurrences. Studying the correlations among multiple diseases provides an important avenue to better understand the common genetic background of diseases and to help develop new drugs that can treat multiple diseases. Meanwhile, network science has seen increasing applications on modeling complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases. In this article, known disease-gene associations were represented using a weighted bipartite network. We extracted a weighted human diseases network from such a bipartite network to show the correlations of diseases. Subsequently, we proposed a new centrality measurement for the weighted human disease network (WHDN) in order to quantify the importance of diseases. Using our centrality measurement to quantify the importance of vertices in WHDN, we were able to find a set of most central diseases. By investigating the 30 top diseases and their most correlated neighbors in the network, we identified disease linkages including known disease pairs and novel findings. Our research helps better understand the common genetic origin of human diseases and suggests top diseases that likely induce other related diseases.
Highlights
During the past decades, significant progress has been made in our understanding of human diseases [1]
We propose a new method for the construction of a weighted human disease network(WHDN) and a new centrality measure to identify the most important diseases
We first clean up the data in order to ensure that all diseases and genes in the dataset are unique and that there is no replication of disease-gene associations
Summary
Significant progress has been made in our understanding of human diseases [1]. The weight of links represented the similarity of symptoms between two diseases They showed that the correlations among diseases were significantly related to the genetic associations that each pair of diseases had in common as well as the interactions between their related proteins. Martinez et al [43] proposed a generic vertex prioritization method using the idea of propagating information across data networks and measuring the correlation between the propagated values for a query and a target set of entities The authors tested their method by ranking disease genes associated with Alzheimer’s disease, diabetes mellitus type 2 and breast cancer. We propose a new method for the construction of a weighted human disease network(WHDN) and a new centrality measure to identify the most important diseases. We present the top 30 diseases ranked by our centrality measure in our WHDN and discuss their biological implications
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