Inferring biological networks with output kernel trees

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 ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc
• Keywords: Biological Networks, BMCBI 2007, Protein-protein Interaction Networks, Systems Biology