Revealing Disease Similarities by Text Mining

Texts written in human language contain structured information that is not easily parsable by computers. Text mining relies on large text corpora to derive rules which can be used by automatic means to extract automatically such information. Scientific literature represents the main source of information to study any biological phenomenon. While some phenomenon are studied to the point that corpora can actually be build, scientific literature describing rare diseases is scarce implying an even bigger challenge for automatic approaches. In order to tackle this problem the ELIXIR infrastructure is supporting various initiatives for data integration in different field of life sciences, including rare diseases, which will pave the way to the development of dedicated pieces of software. In this work we present a tool which applies a text-mining strategy to multiple text sets and merges individual results in order to infer not explicitly written connections.