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Title: The fully automated construction of metabolic pathways using text mining and knowledge-based constraints
Author: Czarnecki, Jan Michael
ISNI:       0000 0004 5367 2054
Awarding Body: Birkbeck (University of London)
Current Institution: Birkbeck (University of London)
Date of Award: 2015
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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 by inheriting annotations from other organisms where the pathway has been curated. Due to the lack of curated data there has been no large scale study to assess the accuracy of current methods for inheriting metabolic pathway annotations. In this thesis I describe the development of the Literature Metabolic Pathway Extraction Tool (LiMPET), a text-mining tool designed for the automated extraction of metabolic pathways from article abstracts and full-text open-access articles. I propose the use of LiMPET by metabolic pathway curators to increase the rate of curation and by individual researchers interested in a particular pathway. The mining of metabolic pathways from the literature has been largely neglected by the textmining community. The work described in this thesis shows the tractability of the problem, however, and it is my hope that it attracts more research into the area.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available