Czarnecki, J. and Shepherd, Adrian J. (2014) Mining biological networks from full-text articles. Methods in Molecular Biology 1159 , pp. 135-145. ISSN 1064-3745.
Abstract
The study of biological networks is playing an increasingly important role in the life sciences. Many different kinds of biological system can be modelled as networks; perhaps the most important examples are protein–protein interaction (PPI) networks, metabolic pathways, gene regulatory networks, and signalling networks. Although much useful information is easily accessible in publicly databases, a lot of extra relevant data lies scattered in numerous published papers. Hence there is a pressing need for automated text-mining methods capable of extracting such information from full-text articles. Here we present practical guidelines for constructing a text-mining pipeline from existing code and software components capable of extracting PPI networks from full-text articles. This approach can be adapted to tackle other types of biological network.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | Biomedical Literature Mining, Edited by Kunar, V.D. and Tipney, H.J. ISBN: 9781493907083 |
| Keyword(s) / Subject(s): | Named entity recognition, Relationship extraction, Biological networks, Protein–protein interactions |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Natural Sciences |
| Research Centres and Institutes: | Bioinformatics, Bloomsbury Centre for (Closed), Structural Molecular Biology, Institute of (ISMB) |
| Depositing User: | Administrator |
| Date Deposited: | 07 May 2014 15:47 |
| Last Modified: | 02 Aug 2023 17:10 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/9694 |
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