Thousands of new scientific articles are published every day, which pile up exponentially with the other millions of papers already deposited in the literature. Keeping up with the scientific state-of-the-art has become an overwhelming task for researchers even in their own fields and subfields, let alone in other areas of science. In this scenario, computational methods such as text-mining, machine learning, and cognitive computing are becoming invaluable to make the chore of summarising published scientific literature less daunting. In this project, we utilized cognitive computing text-mining application to extract known relationships between genes and a broad range of diseases from the peer-reviewed literature. We built knowledge networks of genes and diseases and tracked the evolution of these relationships yearly.