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Bioinformatics Advance Access published online on February 10, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth061
Bioinformatics © Oxford University Press 2004; all rights reserved
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Revised July 29, 2003
Accepted September 3, 2003

Article

Probabilistic inference of molecular networks from noisy data sources

Ivan Iossifov 1, Michael Krauthammer 1, Carol Friedman 2, Vasileios Hatzivassiloglou 3, Joel S. Bader 4, Kevin P. White 5, Andrey Rzhetsky 1

1 Department of Biomedical Informatics, Columbia University New York, NY 10032, USA; Columbia Genome Center, Columbia University New York, NY 10032, USA
2 Department of Biomedical Informatics, Columbia University New York, NY 10032, USA
3 Department of Computer Science, Columbia University New York, NY 10027, USA
4 CuraGen Corporation New Haven, CT 06511, USA
5 Department of Genetics, Yale University School of Medicine New Haven, CT 06520, USA


  Abstract

Information on molecular networks, such as networks of interacting proteins, comes from diverse sources that contain remarkable differences in distribution and quantity of errors. Here we introduce a probabilistic model useful for predicting protein interactions from heterogeneous data sources. The model describes stochastic generation of protein-protein interaction networks with real-world properties, as well as generation of two heterogeneous sources of protein-interaction information: research results automatically extracted from literature and yeast twohybrid experiments. Based on the domain composition of proteins, we use the model to predict protein interactions for pairs of proteins for which no experimental data are available. We further explore the prediction limits given experimental data that cover only part of the underlying protein networks. This approach can be extended naturally to include other types of biological data sources.


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