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キーワード:
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要旨:
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.
