Understanding the relationship between diseases based on the underlying biological mechanisms isone of the greatest challenges in modern biology and medicine. Exploring disease-diseaseassociations by using system-level biological data is expected to improve our current knowledge ofdisease relationships, which may lead to further improvements in disease diagnosis, prognosis andtreatment.
Results:
We took advantage of diverse biological data including disease-gene associations and a large-scalemolecular network to gain novel insights into disease relationships.
We analysed and compared fourpublicly available disease-gene association datasets, then applied three disease similarity measures,namely annotation-based measure, function-based measure and topology-based measure, to estimatethe similarity scores between diseases. We systematically evaluated disease associations obtained bythese measures against a statistical measure of comorbidity which was derived from a large numberof medical patient records.
Our results show that the correlation between our similarity measures andcomorbidity scores is substantially higher than expected at random, confirming that our similaritymeasures are able to recover comorbidity associations. We also demonstrated that our predicteddisease associations correlated with disease associations generated from genome-wide associationstudies significantly higher than expected at random.
Furthermore, we evaluated our predicteddisease associations via mining the literature on PubMed, and presented case studies to demonstratehow these novel disease associations can be used to enhance our current knowledge of diseaserelationships.
Conclusion:
We present three similarity measures for predicting disease associations. The strong correlationbetween our predictions and known disease associations demonstrates the ability of our measures toprovide novel insights into disease relationships.
Author: Kai SunJoana P GoncalvesChris LarminieNata¿a Pr¿ulj
Credits/Source: BMC Bioinformatics 2014, 15:304
Published on: 2014-09-17
Tweet
News Provider: 7thSpace Interactive / EUPB Press Office
Social Bookmarking
RETWEET This! | Digg this! | Post to del.icio.us | Post to Furl | Add to Netscape | Add to Yahoo! | Rojo
There are no comments available. Be the first to write a comment.