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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology

Open Access Open Badges Research

A vertex similarity-based framework to discover and rank orphan disease-related genes

Cheng Zhu1, Akash Kushwaha1, Kenneth Berman1 and Anil G Jegga123*

Author Affiliations

1 Department of Computer Science, University of Cincinnati, Cincinnati, Ohio 45229, USA

2 Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio 45229, USA

3 Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH-45229, USA

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BMC Systems Biology 2012, 6(Suppl 3):S8  doi:10.1186/1752-0509-6-S3-S8

Published: 17 December 2012



A rare or orphan disease (OD) is any disease that affects a small percentage of the population. While opportunities now exist to accelerate progress toward understanding the basis for many more ODs, the prioritization of candidate genes is still a critical step for disease-gene identification. Several network-based frameworks have been developed to address this problem with varied results.


We have developed a novel vertex similarity (VS) based parameter-free prioritizing framework to identify and rank orphan disease candidate genes. We validate our approach by using 1598 known orphan disease-causing genes (ODGs) representing 172 orphan diseases (ODs). We compare our approach with a state-of-art parameter-based approach (PageRank with Priors or PRP) and with another parameter-free method (Interconnectedness or ICN). Our results show that VS-based approach outperforms ICN and is comparable to PRP. We further apply VS-based ranking to identify and rank potential novel candidate genes for several ODs.


We demonstrate that VS-based parameter-free ranking approach can be successfully used for disease candidate gene prioritization and can complement other network-based methods for candidate disease gene ranking. Importantly, our VS-ranked top candidate genes for the ODs match the known literature, suggesting several novel causal relationships for further investigation.