Following closely the explosion of genomics data, the number of scientific publications in the biomedical sector has seen an exponential increase. This highlighted the necessity for tools and infrastructures that would automatically process and extract the information recorded in journal articles, using a standardized vocabulary with ontological relationships. In this direction, we have developed tools for extracting and analyzing information from literature, including PubNet, a web-based application used to extract and integrate information from PubMed providing a graphical visualization of complex networks in order to infer functional similarities, and YeastHub, a semantic web use case for integrating data in the life sciences domain. Moreover, we proposed structures for digital publications that facilitate automated literature mining; analyzed trends in biomedical publications on a large scale
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