Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach Articles uri icon

authors

  • GARCIA JIMENEZ, BEATRIZ
  • JUAN, DAVID
  • EZKURDIA, IAKES
  • ANDRES LEÓN, EDUARDO
  • VALENCIA, ALFONSO

publication date

  • April 2010

issue

  • 4

volume

  • 5

International Standard Serial Number (ISSN)

  • 1932-6203

abstract

  • Background: Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome. Methodology/Principal Findings: We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/.