de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Recent Approaches to the Prioritization of Candidate Disease Genes

MPS-Authors
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;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


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