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Methods Mol Biol. 2009;541:449-61. doi: 10.1007/978-1-59745-243-4_19.

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

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Informatics and Technology Complex (IT), Indiana University School of Informatics, IUPUI, Indianapolis, IN, USA.


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.

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