Extraction of regulatory gene/protein networks from Medline.
BACKGROUND
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 3,319 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.


BACKGROUND
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
Saric
Jasmin
J
EML Research gGmbH D-69118 Heidelberg, Germany. saric@eml-r.org
Jensen
Lars Juhl
LJ
Ouzounova
Rossitza
R
Rojas
Isabel
I
Bork
Peer
P
D000043:Abstracting and Indexing / Q000379:methods; D005786:Gene Expression Regulation / Q000502:physiology*; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010506:Periodicals as Topic; D029701:Saccharomyces cerevisiae Proteins / Q000145:classification / Q000235:genetics* / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D029701:Saccharomyces cerevisiae Proteins