A Pragmatic Information Extraction Strategy for Gathering Data on Genetic Interactions
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Protein names precisely peeled off free text
Bioinformatics
High-recall protein entity recognition using a dictionary
Bioinformatics
Wrap-Up: a trainable discourse module for information extraction
Journal of Artificial Intelligence Research
Methodological Review: Biomedical text mining and its applications in cancer research
Journal of Biomedical Informatics
Text Mining of Protein Phosphorylation Information Using a Generalizable Rule-Based Approach
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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The rapid growth of scientific literature calls for automatic and efficient ways to facilitate extracting experimental data on protein phosphorylation. Such information is of great value for biologists in studying cellular processes and diseases such as cancer and diabetes. Existing approaches like RLIMS-P are mainly rule based. The performance lays much reliance on the completeness of rules. We propose an SVM-based system known as MinePhos which outperforms RLIMS-P in both precision and recall of information extraction when tested on a set of articles randomly chosen from PubMed.