Ma Xiaotu, Lee Hyunju, Wang Li, Sun Fengzhu
Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA.
Bioinformatics. 2007 Jan 15;23(2):215-21. doi: 10.1093/bioinformatics/btl569. Epub 2006 Nov 10.
Identifying candidate genes associated with a given phenotype or trait is an important problem in biological and biomedical studies. Prioritizing genes based on the accumulated information from several data sources is of fundamental importance. Several integrative methods have been developed when a set of candidate genes for the phenotype is available. However, how to prioritize genes for phenotypes when no candidates are available is still a challenging problem.
We develop a new method for prioritizing genes associated with a phenotype by Combining Gene expression and protein Interaction data (CGI). The method is applied to yeast gene expression data sets in combination with protein interaction data sets of varying reliability. We found that our method outperforms the intuitive prioritizing method of using either gene expression data or protein interaction data only and a recent gene ranking algorithm GeneRank. We then apply our method to prioritize genes for Alzheimer's disease.
The code in this paper is available upon request.
在生物学和生物医学研究中，识别与给定表型或性状相关的候选基因是一个重要问题。基于来自多个数据源的累积信息对基因进行优先级排序至关重要。当有一组针对该表型的候选基因时，已经开发了几种综合方法。然而，当没有候选基因时，如何对表型的基因进行优先级排序仍然是一个具有挑战性的问题。
我们开发了一种通过结合基因表达和蛋白质相互作用数据（CGI）对与表型相关的基因进行优先级排序的新方法。该方法应用于酵母基因表达数据集，并结合不同可靠性的蛋白质相互作用数据集。我们发现我们的方法优于仅使用基因表达数据或蛋白质相互作用数据的直观优先级排序方法以及最近的基因排名算法GeneRank。然后我们应用我们的方法对阿尔茨海默病的基因进行优先级排序。
本文中的代码可根据要求提供。
