Bayesian inference of protein-protein interactions from biological literature.
BACKGROUND
Protein-protein interaction (PPI) extraction from published biological articles has attracted much attention because of the importance of protein interactions in biological processes. Despite significant progress, mining PPIs from literatures still rely heavily on time- and resource-consuming manual annotations.


RESULTS
In this study, we developed a novel methodology based on Bayesian networks (BNs) for extracting PPI triplets (a PPI triplet consists of two protein names and the corresponding interaction word) from unstructured text. The method achieved an overall accuracy of 87% on a cross-validation test using manually annotated dataset. We also showed, through extracting PPI triplets from a large number of PubMed abstracts, that our method was able to complement human annotations to extract large number of new PPIs from literature.


BACKGROUND
Programs/scripts we developed/used in the study are available at http://stat.fsu.edu/~jinfeng/datasets/Bio-SI-programs-Bayesian-chowdhary-zhang-liu.zip.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Statistics, Harvard University, Cambridge, MA 02138, USA. jliu@stat.harvard.edu
Rajesh
R
Chowdhary
Jinfeng
J
Zhang
Jun S
JS
Liu
D001499:Bayes Theorem; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
D011506:Proteins