Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information.
BACKGROUND
The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest.


RESULTS
This paper reports a scalable method for the discovery of protein-protein interactions in Medline abstracts, using a combination of text analytics, statistical and graphical analysis, and a set of easily implemented rules. Applying these techniques to 12,300 abstracts, a precision of 0.61 and a recall of 0.97 were obtained, (f = 0.74) and when allowing for two-hop and three-hop relations discovered by graphical analysis, the precision was 0.74 (f = 0.83).


CONCLUSIONS
This combination of linguistic and statistical approaches appears to provide the highest precision and recall thus far reported in detecting protein-protein relations using text analytic approaches.
Cooper
James W
JW
Text Analytics, IBM Thomas J Watson Research Center, PO Box 704, Yorktown Heights, NY 10598, USA. jwcnmr@watson.ibm.com
Kershenbaum
Aaron
A
D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003628:Database Management Systems; D006801:Humans; D016247:Information Storage and Retrieval; D008037:Linguistics; D016239:MEDLINE; D015233:Models, Statistical; D008968:Molecular Conformation; D009323:Natural Language Processing; D011381:Programming Languages; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry; D040901:Proteomics; D012984:Software; D009626:Terminology as Topic; D018875:Vocabulary, Controlled
D011506:Proteins