Predicting protein complex membership using probabilistic network reliability

Genome Res. 2004 Jun;14(6):1170-5. doi: 10.1101/gr.2203804. Epub 2004 May 12.

Abstract

Evidence for specific protein-protein interactions is increasingly available from both small- and large-scale studies, and can be viewed as a network. It has previously been noted that errors are frequent among large-scale studies, and that error frequency depends on the large-scale method used. Despite knowledge of the error-prone nature of interaction evidence, edges (connections) in this network are typically viewed as either present or absent. However, use of a probabilistic network that considers quantity and quality of supporting evidence should improve inference derived from protein networks. Here we demonstrate inference of membership in a partially known protein complex by using a probabilistic network model and an algorithm previously used to evaluate reliability in communication networks.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Fungal Proteins / chemistry
  • Macromolecular Substances
  • Neural Networks, Computer*
  • Predictive Value of Tests
  • Probability*
  • Protein Interaction Mapping / methods
  • Protein Interaction Mapping / statistics & numerical data
  • Reproducibility of Results
  • Software

Substances

  • Fungal Proteins
  • Macromolecular Substances