The power of protein interaction networks for associating genes with diseases

Navlakha, S., Kingsford, C. (April 2010) The power of protein interaction networks for associating genes with diseases. Bioinformatics, 26 (8). pp. 1057-63. ISSN 1367-4803 (Public Dataset)

DOI: 10.1093/bioinformatics/btq076


MOTIVATION: 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. RESULTS: 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. AVAILABILITY: The predictions made by each algorithm considered are available online at

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > design > protein network design
bioinformatics > computational biology > algorithms
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Matthew Dunn
Date: 15 April 2010
Date Deposited: 06 Nov 2019 20:49
Last Modified: 06 Nov 2019 20:49
PMCID: PMC2853684
Related URLs:
Dataset ID:
  • Algorithm predictions

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