Send to:

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2010 Apr 15;26(8):1057-63. doi: 10.1093/bioinformatics/btq076. Epub 2010 Feb 24.

The power of protein interaction networks for associating genes with diseases.

Author information

  • 1Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies and Department of Computer Science, University of Maryland College Park, College Park, MD 20742, USA.



Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques.


We assessed the utility of physical protein interactions for determining gene-disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required.


The predictions made by each algorithm considered are available online at

[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk