Predicting disease associations via biological network analysis

BMC Bioinformatics

Nataša Pržulj²  Chris Larminie¹  Joana P Gonçalves²  Kai Sun² 

[1]Computational Biology, GlaxoSmithKline, Stevenage, Hertfordshire, SG1 2NY, UK

[2]Department of Computing, Imperial College London, London, SW7 2AZ, UK

关键词: Protein-protein interaction;    Topology;    Graph theory;    Network analysis;    Disease classification;   

Others  :  1086024
DOI  :  10.1186/1471-2105-15-304

 received in 2013-12-09, accepted in 2014-08-19,  发布年份 2014

【 摘 要 】

Background

Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment.

Results

We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships.

Conclusions

We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.

【 授权许可】

   
2014 Sun et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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