http://www.dovepress.com/articles.php?article_id=8144
Understanding the genetic foundations of genetic diseases, such as cancer, Alzheimer disease, or Huntington’s disease, is critical to the development of new diagnostics and treatments. Several computational methods have been used to speed up the discovery process, eg, by selecting the molecular targets for a given disease. However, despite the achievements obtained over recent years, better solutions are still required. This paper presents an innovative computational method that addresses the problem of using disperse biomedical knowledge to select the best candidate genes associated with a disease. The method uses a network representation of current biomedical knowledge that includes biomolecular concepts such as genes, diseases, pathways, and biological process. It also applies information extraction techniques to enrich the network with more dynamic and updated data. A biologically inspired algorithm is applied to this network in order to identify association levels between genes and diseases. The solution proposed here surpasses many limitations of previous methods such as the need for training data. The validation applied demonstrates that the proposed method has best overall results compared with state-of-the-art methods as it also performs especially well for the critical top-rank positions. We believe this method represents a major advance over previous work and that it will be a key tool for future gene–disease association studies.
gene–disease, biomedical networks, prioritization, computational method
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