Background: Understanding the relationship between diseases based on the underlying biological mechanisms is\none of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using\nsystem-level biological data is expected to improve our current knowledge of disease relationships, which may lead to\nfurther improvements in disease diagnosis, prognosis and treatment.\nResults: We took advantage of diverse biological data including disease-gene associations and a large-scale\nmolecular network to gain novel insights into disease relationships. We analysed and compared four publicly available\ndisease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure,\nfunction-based measure and topology-based measure, to estimate the similarity scores between diseases. We\nsystematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity\nwhich was derived from a large number of medical patient records. Our results show that the correlation between our\nsimilarity measures and comorbidity scores is substantially higher than expected at random, confirming that our\nsimilarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease\nassociations correlated with disease associations generated from genome-wide association studies significantly\nhigher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the\nliterature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used\nto enhance our current knowledge of disease relationships.\nConclusions: We present three similarity measures for predicting disease associations. The strong correlation\nbetween our predictions and known disease associations demonstrates the ability of our measures to provide novel\ninsights into disease relationships.
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