DeCoaD: determining correlations among diseases using protein interaction networks

Abstract

Background: Disease-disease similarities can be investigated from multiple perspectives. Identifying similar diseases based on the underlying biomolecular interactions can be especially useful, because it may shed light on the common causes of the diseases and therefore may provide clues for possible treatments. Here we introduce DeCoaD, a web-based program that uses a novel method to assign pair-wise similarity scores, called correlations, to genetic diseases.

Findings: DeCoaD uses a random walk to model the flow of information in a network within which nodes are either diseases or proteins and links signify either protein-protein interactions or disease-protein associations. For each protein node, the total number of visits by the random walker is called the weight of that node. Using a disease as both the starting and the terminating points of the random walks, a corresponding vector, whose elements are the weights associated with the proteins, can be constructed. The similarity between two diseases is defined as the cosine of the angle between their associated vectors. For a user-specified disease, DeCoaD outputs a list of similar diseases (with their corresponding correlations), and a graphical representation of the disease families that they belong to. Based on a probabilistic clustering algorithm, DeCoaD also outputs the clusters that the disease of interest is a member of, and the corresponding probabilities. The program also provides an interface to run enrichment analysis for the given disease or for any of the clusters that contains it.

Conclusions: DeCoaD uses a novel algorithm to suggest non-trivial similarities between diseases with known gene associations, and also clusters the diseases based on their similarity scores. DeCoaD is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/mn/DeCoaD/.

Figure 1

Graphical summary of DeCoaD. The graphical summary of the results when the input disease is Retinitis Pigmentosa 7 (RP7) (MeSH ID: C564284). For each disease represented by a leaf node, the blue color intensity indicates the correlation strength with the input disease RP7. The non-leaf nodes, always displayed in white, are never included in the calculation. They are only shown to reflect the curated hierarchical structure of the disease families containing the identified similar diseases (nodes in blue) in the CTD disease database.

Figure 2

Another Graphical summary of DeCoaD. The graphical summary of the results when the input disease is Fundus Albipunctatus (MeSH ID: C562733). This input is one of the diseases reported as being similar to RP7 in Figure 1.

Figure 3

Diseases similar to Evr4. When Erv4 (an eye disease) is given as an input and the lowest rank cutoff is set to 5, the identified similar diseases are from a different family (musculoskeletal diseases). However, the diseases are all related to the Wnt signaling pathway.

Figure 4

Enrichment results for Evr4 and the corresponding top ranking cluster. The top-ranking GO and KEGG terms found by the enrichment analysis are shown for Evr4 (a) and for the cluster that includes Evr4 with the highest probability (b). Although terms with E values less than ${10}^{-3}$ are deemed significant, we only display here terms with E values less than ${10}^{-5}$ to avoid crowdedness. The readers can see the whole list by running the SaddleSum interface on the DeCoaD results page.

Figure 5

Enrichment results for the top ranking clusters associated with RP7 and Fundus Albipunctatus. The top-ranking GO and KEGG terms found by the enrichment analysis are shown for the top ranking clusters associated with RP7 (a) and Fundus Albipunctatus (b). Although terms with E values less than ${10}^{-3}$ are deemed significant, we only display here terms with E values less than ${10}^{-4}$ to avoid crowdedness. The readers can see the whole list by running the SaddleSum interface on the DeCoaD results page.