Connecting protein interaction data, mutations, and disease using bioinformatics.

Academic Article

Abstract

  • Understanding how mutations lead to changes in protein function and/or protein interaction is critical to understanding the molecular causes of clinical phenotypes. In this method, we present a path toward integration of protein interaction data and mutation data and then demonstrate the identification of a subset of proteins and interactions that are important to a particular disease. We then build a statistical model of disease mutations in this disease-associated subset of proteins, and visualize these results. Using Alzheimer's disease (AD) as case implementation, we find that we are able to identify a subset of proteins involved in AD and discriminate disease-associated mutations from SNPs in these proteins with 83% accuracy. As the molecular causes of disease become more understood, models such as these will be useful for identifying candidate variants most likely to be causative.
  • Published In

    Author List

  • Chen JY; Youn E; Mooney SD
  • Start Page

  • 449
  • End Page

  • 461
  • Volume

  • 541