Max Planck Institute for Informatics, Saarbrücken, Germany.
Wiley Interdiscip Rev Syst Biol Med. 2012 Sep-Oct;4(5):429-42. doi: 10.1002/wsbm.1177. Epub 2012 Jun 11.
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.
许多努力仍致力于发现与特定表型（尤其是疾病）相关的基因。因此，经常应用高通量技术来检测数十甚至数百个候选基因。然而，对许多候选基因的实验验证通常是一项昂贵且耗时的任务。因此，已经开发了各种计算方法来支持确定最有希望的候选基因进行后续研究。通常利用与目标疾病和相关基因有关的生物医学知识来寻找新的基因-疾病关联并对候选基因进行优先级排序。在本文中，我们重点介绍候选基因优先级排序这一研究领域的最新方法学进展。我们专注于使用网络信息并整合异质数据源的方法。此外，我们还讨论了当前用于评估和比较不同优先级排序方法的基准测试程序。
