This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Improving Protein-Protein Interaction Pair Ranking with an Integrated Global Association Score
Nov.-Dec. 2012 (vol. 9 no. 6)
pp. 1690-1695
Richard Tzong-Han Tsai, Dept. of Comput. Sci. & Eng., Yuan Ze Univ., Chungli, Taiwan
Protein-protein interaction (PPI) database curation requires text-mining systems that can recognize and normalize interactor genes and return a ranked list of PPI pairs for each article. The order of PPI pairs in this list is essential for ease of curation. Most of the current PPI pair ranking approaches rely on association analysis between the two genes in the pair. However, we propose that ranking an extracted PPI pair by considering both the association between the paired genes and each of those genes' global associations with all other genes mentioned in the paper can provide a more reliable ranked list. In this work, we present a composite interaction score that considers not only the association score between two interactors (pair association score) but also their global association scores. We test three representative data fusion algorithms to estimate this global association score-two Borda-Fuse models and one linear combination model (LCM). The three estimation methods are evaluated using the data set of the BioCreative II.5 Interaction Pair Task (IPT) in terms of area under the interpolated precision/recall curve (AUC iP/R). Our experimental results indicate that using LCM to estimate the global association score can boost the AUC iP/R score from 0.0175 to 0.2396, outperforming the best BioCreative II.5 IPT system.
Index Terms:
proteomics,bioinformatics,data mining,genetics,molecular biophysics,proteins,BioCreative II.5 IPT system,protein-protein interaction pair ranking,integrated global association score,PPI,text-mining systems,interactor genes,database curation,data fusion algorithms,Borda-Fuse models,one linear combination model,AUC,iP-R score,Text mining,Databases,Bioinformatics,Proteins,Mutual information,Bioinformatics,Protein engineering,bioinformatics databases,Text mining
Citation:
Richard Tzong-Han Tsai, "Improving Protein-Protein Interaction Pair Ranking with an Integrated Global Association Score," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 6, pp. 1690-1695, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.99
Usage of this product signifies your acceptance of the Terms of Use.