title: Metabolic Pathway Mining.
abstract: Understanding metabolic pathways is one of the most important fields in bioscience in the post-genomic era, but curating metabolic pathways requires considerable man-power. As such there is a lack of reliable, experimentally verified metabolic pathways in databases and databases are forced to predict all but the most immediately useful pathways.Text-mining has the potential to solve this problem, but while sophisticated text-mining methods have been developed to assist the curation of many types of biomedical networks, such as protein-protein interaction networks, the mining of metabolic pathways from the literature has been largely neglected by the text-mining community. In this chapter we describe a pipeline for the extraction of metabolic pathways built on freely available open-source components and a heuristic metabolic reaction extraction algorithm.
authors.affiliation: School of Biosciences, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK.
authors.forename: Jan M
authors.initials: JM
authors.lastname: Czarnecki
authors.affiliation: Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK. a.shepherd@mail.cryst.bbk.ac.uk.
authors.forename: Adrian J
authors.initials: AJ
authors.lastname: Shepherd
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D009323:Natural Language Processing; D011485:Protein Binding
keywords: Information extraction; Metabolic interaction extraction; Metabolic pathway; Named entity recognition; Natural language processing; Text-mining