Extending Diseasome by Integrating the Knowledge from Distributed Databases

Analysing large amounts of biomedical data is the new challenge in the post-genomic era. One of the goals in gene research is the computation of the similarity between diseases based on the genes they are related to. Identifying biomedical relationships between diseases can lead to finding of new drugs and medicaments. The human disease network (Diseasome) illustrates the association between diseases based on genes these diseases share. A disadvantage of this network is the data itself, as Diseasome is based only on a single database (OMIM). There exist, however, a large number of other biomedical databases, and integrating them, in order to be able to profit from all their data, is an impossible task. Thus, we propose a different approach, namely, to focus only on the integration of the knowledge of all these databases. In our approach, we extend Diseasome by integrating the knowledge from other distributed databases, without needing to integrate the data itself. To compute the similarity between diseases we apply data mining techniques.