GeneRank: using search engine technology for the analysis of microarray experiments.
GeneRank: using search engine technology for the analysis of microarray experiments.
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DOI：
10.1186/1471-2105-6-233
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发表时间：
2005-09-21
影响因子：
3
通讯作者：
Gilbert DR
中科院分区：
文献类型：
--
作者：
Morrison JL;Breitling R;Higham DJ;Gilbert DR
简单的微阵列实验的解释通常是基于参考和“处理”样品之间基因表达的倍数变化，其中处理可以是从药物暴露到遗传变异的许多类型。对结果的解释通常结合了差异表达基因的列表和先前关于其生物学功能的知识。在这里，我们评估的方法-流行的搜索引擎谷歌采用的PageRank算法的基础上-试图自动化的一些程序，通过利用生物背景信息生成优先级的基因列表。GeneRank是PageRank的直观修改，保留了其许多数学属性。它将基因表达信息与来自基因注释（基因本体）或表达谱相关性的网络结构相结合。使用模拟和真实的数据，我们发现，该算法提供了一个改进的排名基因相比，纯表达变化的排名。我们修改的PageRank算法提供了一种替代方法来评估微阵列实验结果，结合了有关底层网络的先验知识。与仅根据实验观察到的倍数变化评估基因的重要性相比，GeneRank提供了改进，并且可以用作进一步分析开发的基础。
Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method – based on the PageRank algorithm employed by the popular search engine Google – that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies) or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments.
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DOI：
10.1016/s0097-8485(96)80004-0
发表时间：
1996-03-01
期刊：
COMPUTERS & CHEMISTRY
影响因子：
--
作者：
Gribskov, M;Robinson, NL
通讯作者：
Robinson, NL
影响因子：
3
作者：
Breitling, R;Amtmann, A;Herzyk, P
通讯作者：
Herzyk, P
影响因子：
64.8
作者：
Iyer, VR;Horak, CE;Brown, PO
通讯作者：
Brown, PO
影响因子：
56.9
作者：
Ren, B;Robert, F;Young, RA
通讯作者：
Young, RA
影响因子：
56.9
作者：
DeRisi, JL;Iyer, VR;Brown, PO
通讯作者：
Brown, PO