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Recent approaches to the prioritization of candidate disease genes

Authors

  • Nadezhda T. Doncheva,

    1. Max Planck Institute for Informatics, Saarbrücken, Germany
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    • These authors contributed equally to this work and should be regarded as joint first authors.

  • Tim Kacprowski,

    1. Max Planck Institute for Informatics, Saarbrücken, Germany
    2. University Medicine Greifswald, Greifswald, Germany
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    • These authors contributed equally to this work and should be regarded as joint first authors.

  • Mario Albrecht

    Corresponding author
    1. Max Planck Institute for Informatics, Saarbrücken, Germany
    2. University Medicine Greifswald, Greifswald, Germany
    • Max Planck Institute for Informatics, Saarbrücken, Germany
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Abstract

Many efforts are still devoted to the discovery of genes involved with specific phenotypes, in particular, diseases. High-throughput techniques are thus applied frequently to detect dozens or even hundreds of candidate genes. However, the experimental validation of many candidates is often an expensive and time-consuming task. Therefore, a great variety of computational approaches has been developed to support the identification of the most promising candidates for follow-up studies. The biomedical knowledge already available about the disease of interest and related genes is commonly exploited to find new gene–disease associations and to prioritize candidates. In this review, we highlight recent methodological advances in this research field of candidate gene prioritization. We focus on approaches that use network information and integrate heterogeneous data sources. Furthermore, we discuss current benchmarking procedures for evaluating and comparing different prioritization methods. WIREs Syst Biol Med 2012. doi: 10.1002/wsbm.1177

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