Chapter title |
Metabolic Pathway Mining.
|
---|---|
Chapter number | 8 |
Book title |
Bioinformatics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6613-4_8 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6611-0, 978-1-4939-6613-4
|
Authors |
Jan M. Czarnecki, Adrian J. Shepherd |
Editors |
Jonathan M. Keith |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 4 | 50% |
Student > Postgraduate | 2 | 25% |
Student > Master | 2 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 3 | 38% |
Computer Science | 2 | 25% |
Biochemistry, Genetics and Molecular Biology | 1 | 13% |
Social Sciences | 1 | 13% |
Unknown | 1 | 13% |