Background: Biomedical sciences use a variety of data sources on drug molecules,
genes, proteins, diseases and scientific publications etc. This system can be best pictured
as a giant data-network linked together by physical, functional, logical and similarity
relationships. A new hypothesis or discovery can be considered as a new link that can be
deduced from the existing connections. For instance, interactions of two pharmacons - if not
already known - represent a testable novel hypothesis. Such implicit effects are especially
important in complex diseases such as cancer.
Methods: The method we applied was to test whether novel drug combinations or novel
biomarkers can be predicted from a network of existing oncological databases. We start
from the hypothesis that novel, implicit links can be discovered between the network neighborhoods
of data items.
Results: We showed that the overlap of network neighborhoods is strongly correlated with the pairwise interaction
strength of two pharmacons used in cancer therapy, and it is also well correlated with clinical data. In a second
case study we employed this strategy to the discovery of novel biomarkers based on text analysis. In 2012 we
prioritized 10 potential biomarkers for ovarian cancers, 2 of which were in fact described as such in the subsequent
Conclusion: The strategy seems to hold promises for prioritizing new drug combinations or new biomarkers for
experimental testing. Its use is naturally limited by the sparsity and the quality of experimental data, however
both of these aspects are expected to improve given the development of current databases.