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This article is part of the supplement: Probabilistic Modeling and Machine Learning in Structural and Systems Biology

Open Access Research

Inferring biological networks with output kernel trees

Pierre Geurts12*, Nizar Touleimat13, Marie Dutreix3 and Florence d'Alché-Buc1*

Author Affiliations

1 IBISC FRE CNRS 2873 & Epigenomics project, GENOPOLE, 523, Place des Terrasses, 91 Evry, France

2 Department of Electrical Engineering and Computer Science & GIGA, University of Liège, Institut Montefiore, Sart Tilman B28, 4000 Liège, Belgium

3 UMR 2027 CNRS-IC, Institut Curie, Bâtiment 110, Centre Universitaire, 91405 Orsay, France

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BMC Bioinformatics 2007, 8(Suppl 2):S4  doi:10.1186/1471-2105-8-S2-S4

Published: 3 May 2007

Abstract

Background

Elucidating biological networks between proteins appears nowadays as one of the most important challenges in systems biology. Computational approaches to this problem are important to complement high-throughput technologies and to help biologists in designing new experiments. In this work, we focus on the completion of a biological network from various sources of experimental data.

Results

We propose a new machine learning approach for the supervised inference of biological networks, which is based on a kernelization of the output space of regression trees. It inherits several features of tree-based algorithms such as interpretability, robustness to irrelevant variables, and input scalability. We applied this method to the inference of two kinds of networks in the yeast S. cerevisiae: a protein-protein interaction network and an enzyme network. In both cases, we obtained results competitive with existing approaches. We also show that our method provides relevant insights on input data regarding their potential relationship with the existence of interactions. Furthermore, we confirm the biological validity of our predictions in the context of an analysis of gene expression data.

Conclusion

Output kernel tree based methods provide an efficient tool for the inference of biological networks from experimental data. Their simplicity and interpretability should make them of great value for biologists.