Name
Abstract | ||
|---|---|---|
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.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.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.Supplementary data are available at Bioinformatics online. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1093/bioinformatics/btp245 | Bioinformatics |
Keywords | Field | DocType |
corresponding interaction word,ppi triplet,bayesian inference,biological article,biological process,large number,protein interaction,bayesian network,biological literature,new ppis,mining ppis,protein name,computational biology,proteins,binding sites,cross validation,bayes theorem,protein protein interaction | Data mining,Protein–protein interaction,Bayesian inference,Computer science,Inference,Bayesian network,Artificial intelligence,Bioinformatics,Machine learning,Bayes' theorem,Scripting language | Journal |
Volume | Issue | ISSN |
25 | 12 | 1367-4811 |
Citations | PageRank | References |
34 | 1.09 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rajesh Chowdhary | 1 | 100 | 5.94 |
| Jinfeng Zhang | 2 | 86 | 10.11 |
| Jun S Liu | 3 | 246 | 14.98 |