Directed indices for exploring gene expression data.
Directed indices for exploring gene expression data.
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用于探索基因表达数据的定向索引。
DOI：
10.1093/bioinformatics/btg079
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Miller,ThomasP
中科院分区：
文献类型：
--
作者：
LeBlanc,Michael;Kooperberg,Charles;Grogan,ThomasM;Miller,ThomasP
动机：具有临床结局数据的大型表达研究可用于分析。一个重要的目标是识别基因或基因簇，其中表达与患者结果相关。虽然聚类方法是有用的数据探索工具，但它们不直接允许将表达数据与临床结果相关联。或者，基于基因的单变量显著性对基因进行排名的方法不包括基因功能或与先前已经鉴定的基因的关系。此外，筛选后，通过潜在的数千个基因，总结估计（如回归系数或错误率）算法应解决潜在的大偏差引入的基因selection.Results：我们开发了一种基因指数技术，概括的方法，排名基因由他们的单变量协会患者的结果。基因排序的基础上，同时将其表达与患者的结果和特定的感兴趣的基因。该技术还可用于提示与患者结局相关的基因表达谱。交叉验证方法被证明是重要的减少偏见，由于自适应基因选择。该方法在最近收集的基于160例弥漫性大细胞淋巴瘤（DLCL）患者的基因表达数据集上进行了说明。可用性：可以在www.example.com获得用R语言编写的实现基因索引的程序：mikel@crab.org http://www.crab.org/papers/Contact
Motivation: Large expression studies with clinical outcome data are becoming available for analysis. An important goal is to identify genes or clusters of genes where expression is related to patient outcome. While clustering methods are useful data exploration tools, they do not directly allow one to relate the expression data to clinical outcome. Alternatively, methods which rank genes based on their univariate significance do not incorporate gene function or relationships to genes that have been previously identified. In addition, after sifting through potentially thousands of genes, summary estimates (e.g. regression coefficients or error rates) algorithms should address the potentially large bias introduced by gene selection.Results: We developed a gene index technique that generalizes methods that rank genes by their univariate associations to patient outcome. Genes are ordered based on simultaneously linking their expression both to patient outcome and to a specific gene of interest. The technique can also be used to suggest profiles of gene expression related to patient outcome. A cross-validation method is shown to be important for reducing bias due to adaptive gene selection. The methods are illustrated on a recently collected gene expression data set based on 160 patients with diffuse large cell lymphoma (DLCL).Availability: A program written in the R language implementing the gene index can be obtained at http://www.crab.org/papers/Contact: mikel@crab.org
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影响因子：
1.8
作者：
Shi H;Schmidt B;Liu W;Müller-Wittig W
通讯作者：
Müller-Wittig W
DOI：
10.1073/pnas.220392197
发表时间：
2000-10-24
影响因子：
11.1
作者：
Butte, AJ;Tamayo, P;Kohane, IS
通讯作者：
Kohane, IS
DOI：
--
发表时间：
2004
期刊：
Proc. Knowledge Exploration in Life Science Informatics KELSI2004. Lecture Notes in Artificial Intelligence 3303
影响因子：
--
作者：
Ando;T.;Imoto;S.;Miyano;S.
通讯作者：
S.
影响因子：
3.3
作者：
Liu, Bing;de la Fuente, Alberto;Hoeschele, Ina
通讯作者：
Hoeschele, Ina
信息互分析的熵效应估计技术
DOI：
10.3166/tsi.30.1217-1246
发表时间：
2011
期刊：
Tech. Sci. Informatiques
影响因子：
--
作者：
Alexandre Venelli
通讯作者：
Alexandre Venelli