Combination of text-mining algorithms increases the performance.
BACKGROUND
Recently, several information extraction systems have been developed to retrieve relevant information out of biomedical text. However, these methods represent individual efforts. In this paper, we show that by combining different algorithms and their outcome, the results improve significantly. For this reason, CONAN has been created, a system which combines different programs and their outcome. Its methods include tagging of gene/protein names, finding interaction and mutation data, tagging of biological concepts and linking to MeSH and Gene Ontology terms.


RESULTS
In this paper, we will present data that show that combining different text-mining algorithms significantly improves the results. Not only is CONAN a full-scale approach that will ultimately cover all of PubMed/MEDLINE, we also show that this universality has no effect on quality: our system performs as well as or better than existing systems.


BACKGROUND
The LDD corpus presented is available by request to the author. The system will be available shortly. For information and updates on CONAN please visit http://www.cs.uu.nl/people/rainer/conan.html.
Malik
Rainer
R
Universiteit Utrecht, Department of Information and Computing Sciences, Padualaan 14, 3584CH Utrecht, The Netherlands. rainer@cs.uu.nl
Franke
Lude
L
Siebes
Arno
A
D000043:Abstracting and Indexing / Q000379:methods*; D003628:Database Management Systems; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010506:Periodicals as Topic; D020543:Proteome / Q000145:classification* / Q000235:genetics / Q000378:metabolism*; D018511:Systems Integration
D020543:Proteome