BioCAD: an information fusion platform for bio-network inference and analysis
TMBIO '06 Proceedings of the 1st international workshop on Text mining in bioinformatics
High-performance information extraction with AliBaba
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
BioNoculars: extracting protein-protein interactions from biomedical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Assigning roles to protein mentions: The case of transcription factors
Journal of Biomedical Informatics
BioProber2.0: a unified biomedical workbench with mining and probing literatures
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Journal of Biomedical Informatics
Measuring prediction capacity of individual verbs for the identification of protein interactions
Journal of Biomedical Informatics
Event extraction for post-translational modifications
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Using text to build semantic networks for pharmacogenomics
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Towards automatic thematic sheets based on discursive categories in biomedical literature
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Towards exhaustive protein modification event extraction
BioNLP '11 Proceedings of BioNLP 2011 Workshop
International Journal of Data Mining and Bioinformatics
Expert Systems with Applications: An International Journal
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Motivation: We have previously developed a rule-based approach for extracting information on the regulation of gene expression in yeast. The biomedical literature, however, contains information on several other equally important regulatory mechanisms, in particular phosphorylation, which we now expanded for our rule-based system also to extract. Results: This paper presents new results for extraction of relational information from biomedical text. We have improved our system, STRING-IE, to capture both new types of linguistic constructs as well as new types of biological information [i.e. (de-)phosphorylation]. The precision remains stable with a slight increase in recall. From almost one million PubMed abstracts related to four model organisms, we manage to extract regulatory networks and binary phosphorylations comprising 3319 relation chunks. The accuracy is 83--90% and 86--95% for gene expression and (de-)phosphorylation relations, respectively. To achieve this, we made use of an organism-specific resource of gene/protein names considerably larger than those used in most other biology related information extraction approaches. These names were included in the lexicon when retraining the part-of-speech (POS) tagger on the GENIA corpus. For the domain in question, an accuracy of 96.4% was attained on POS tags. It should be noted that the rules were developed for yeast and successfully applied to both abstracts and full-text articles related to other organisms with comparable accuracy. Availability: The revised GENIA corpus, the POS tagger, the extraction rules and the full sets of extracted relations are available from http://www.bork.embl.de/Docu/STRING-IE Contact: saric@eml-r.org