Skip to main content


We're creating a new version of this page. See preview

  • Poster presentation
  • Open Access

Prediction and ranking of human protein-protein interactions within a Bayesian framework

BMC Systems Biology20071 (Suppl 1) :P66

  • Published:


  • Human Protein
  • Bayesian Framework
  • High Accuracy Prediction
  • Diverse Cellular Process
  • Interaction Dataset


Protein-protein interactions carry out and regulate many fundamental cellular activities. The comprehensive study of such interactions on a global scale leads to a better understanding of diverse cellular processes and of the molecular mechanisms of diseases when these processes are deregulated. Large scale experimental datasets of the human interactome are becoming available [1] but their coverage is still very low. Bioinformatic predictors can fill this gap and provide high quality interaction datasets.


We investigate the prediction of direct physical interactions between human proteins, by integrating in a Bayesian framework, several different pieces of evidence including orthology, functional features and local network topology, in an attempt to increase the coverage of the known human interactome. We examine the contribution of different features as well as the use of different datasets.


A semi-naïve Bayes network integrating expression data, orthology, protein localization, domain information and local network topology generates the highest accuracy prediction while maintaining a high coverage. We used this predictor to determine the most likely interacting human protein pairs and rank them according to their likelihood of interaction


Our Bayesian predictor has generated tens of thousands of high likelihood human protein interaction predictions. These are being analyzed and compared to currently known interactions.

Authors’ Affiliations

School of Life Sciences Research, Dundee University, Dundee, Scotland, UK


  1. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, et al: Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005, 437: 1173-8. 10.1038/nature04209PubMedView ArticleGoogle Scholar


© Scott and Barton; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.