The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles
Abstract
Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological journals. Contact: friedman.carol@dmi.columbia.edu
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Comparative experiments on learning information extractors for proteins and their interactions
Bunescu, R., Ge, R., Kate, R. J., Marcotte, E. M., Mooney, R. J., Ramani, A. K., & Wong, Y. W. (2005) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.07.016 · Cited by 428
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Metadata
- Authors
- Friedman, C., Kra, P., Yu, H., Krauthammer, M., & Rzhetsky, A.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-06-01
- Volume
- 17
- Issue
- suppl_1
- Pages
- S74-S82
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2158505321
- Cited by
- 507
- References
- 29
- FWCI
- 11.40
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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