Abstract
Identification of relationships among different biological entities, e.g., genes, proteins, diseases, drugs and chemicals, etc, is an important problem for biological researchers. While such information can be extracted from different types of biological data (e.g., gene and protein sequences, protein structures), a significant source of such knowledge is the biological textual research literature which is increasingly being made available as large-scale public-domain electronic databases (e.g., the Medline database). Automated extraction of such relationships (e.g., gene A inhibits protein B) from textual data can significantly enhance biological research productivity by keeping researchers up-to-date with the state-of-the-art in their research domain, by helping them visualize biological pathways, and by generating likely new hypotheses concerning novel interactions some of which can be good candidates for further biological research and validation. In this chapter, we describe the computational problems and their solutions in such automated extraction of relationships, and present some recent advances made in this area.
Published Version
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