Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme
1 Bioinformatics & Medical Informatics Team, Biomedical Research Foundation of the Academy of Athens, Athens, Soranou Efesiou 4, GR-11527, Greece
2 Department of Informatics, Athens University of Economics & Business, Athens, 76 Patision Street, GR-10434, Greece
3 Pattern Recognition Lab, Department of Computer Engineering & Informatics, University of Patras, Patra, Rio, GR-26500, Greece
4 Institut Télécom, Ecole de Télécom ParisTech, Département Informatique et Réseaux, Paris France
BMC Bioinformatics 2011, 12:239 doi:10.1186/1471-2105-12-239Published: 16 June 2011
Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied.
We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database.
The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.