Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients
Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients
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DOI：
10.1093/bioinformatics/btq345
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发表时间：
2010-09-01
期刊：
影响因子：
5.8
通讯作者：
Beissbarth, Tim
中科院分区：
文献类型：
--
作者：
Johannes, Marc;Brase, Jan C.;Beissbarth, Tim
动机：在癌症研究中，高通量基因表达研究的主要目标之一是识别具有预测临床结果潜力的预后基因特征。通常的做法是使用分类方法调查这些问题。然而，标准方法仅仅依赖于基因表达数据，并假设基因是独立的。包括路径知识先验到分类过程中最近已被指示为一个有前途的方法，以提高分类精度以及预后基因signatures.Results的可解释性和再现性：我们提出了一种新的方法，称为加权递归特征消除。它是基于这样的假设，即具有低倍数变化的基因如果与差异表达的基因相连，则对分类器的影响应该增加。我们使用Google的PageRank算法的修改版本来改变SVM-RFE算法的排名标准。我们的方法在包括788个样本的综合乳腺癌数据集上的评估显示，接收器操作者特征曲线下的面积以及所选基因的再现性和可解释性得到了改善。
Motivation: One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures.Results: We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes.