The Protein Journal

, Volume 28, Issue 2, pp 111–115 | Cite as

Prediction of Interacting Protein Pairs from Sequence Using a Bayesian Method



With the development of bioinformatics, more and more protein sequence information has become available. Meanwhile, the number of known protein–protein interactions (PPIs) is still very limited. In this article, we propose a new method for predicting interacting protein pairs using a Bayesian method based on a new feature representation. We trained our model using data on 6,459 PPI pairs from the yeast Saccharomyces cerevisiae core subset. Using six species of DIP database, our model demonstrates an average prediction accuracy of 93.67%. The result showed that our method is superior to other methods in both computing time and prediction accuracy.


Protein–protein interactions Feature vector Bayesian method Amino acid composition 



Protein–protein interactions


Database of interacting proteins


Protein data bank


Support vector machine


Expression profile reliability


Paralogous verification method


True positive


True negative


False positive


False negative


Receiver operating characteristics


The area under the curve



This work was supported partially by the Project of Provincial Natural Scientific Fund from the Bureau of Education of AnHui Provience(Nos. KJ2007B066, KJ2007A087).


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Key Laboratory of Intelligent Computing & Signal Processing, Ministry of EducationAnHui UniversityHefeiChina
  2. 2.Department of Computer Science and TechnologyChaohu CollegeChaohuChina

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