2004/06/22 by Istvan Albert, Reka Albert, Albert, Istvan +1
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Genomics (q-bio.GN) #Molecular Networks (q-bio.MN) #q-bio.GN #q-bio.MN
paper · pdf · doi:10.48550/arxiv.q-bio/0406042
arxiv created 2004/06/22 · arxiv updated 2009/12/01
High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a 'leave-one-out' approach we find average success rates between 20-50% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org