Monogenetic rare diseases in biomedical databases and text mining

Abstract

The testing of pharmacological hypotheses becomes faster and more accurate, but at the same time more difficult than even two decades ago. It takes more time to collect and analyse disease mechanisms and experimental facts in various specialized resources. We discuss a new approach to aggregating individual pieces of information about a single disease using Elsevier's automated text mining technology. The developed algorithm allows for the collection of published facts in a unified format starting only with the name of the disease. A special template, which combines research and clinical descriptions of diseases was developed. The approach was tested, and information was collected for 55 rare monogenic diseases. Clinical, molecular, and pharmacological characteristics of diseases with supporting references from the literature are available in the form of tables and files. Manually curated templates for 10 rare diseases, including top ranked Cystic Fibrosis and Huntington's disease were published to demonstrate the results of the described approach.