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Recent Approaches to the Prioritization of Candidate Disease Genes

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44342

Doncheva,  Nadezhda Tsankova
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44714

Kacprowski,  Tim
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons43993

Albrecht,  Mario
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Zitation

Doncheva, N. T., Kacprowski, T., & Albrecht, M. (2012). Recent Approaches to the Prioritization of Candidate Disease Genes. WIREs Systems Biology and Medicine, 4(5), 429-442. doi:10.1002/wsbm.1177.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-C52B-C
Zusammenfassung
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.