The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the networ
