Ordinal response prediction using bootstrap aggregation, with application to a high-throughput methylation data set.
Ordinal response prediction using bootstrap aggregation, with application to a high-throughput methylation data set.
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DOI：
10.1002/sim.3707
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发表时间：
2009-12-20
影响因子：
2
通讯作者：
Mas, V. R.
中科院分区：
文献类型：
--
作者：
Archer, K. J.;Mas, V. R.
许多进行转化研究的研究人员正在进行高通量基因组实验，然后使用所得的高维数据集开发多基因分类器。在许多应用程序中，要预测的类可能本质上是有序的。顺序结局的例子包括肿瘤-淋巴结-转移（TNM）期（I， II， III， IV）；药物毒性评估为无、轻度、中度或重度；对治疗的反应分为完全缓解、部分缓解、病情稳定或病情进展。虽然可以将标称响应分类方法应用于有序响应数据，但这样做会丢失一些可能提高分类器预测性能的信息。本研究检验了选择性有序分裂函数与自举聚合相结合对有序响应进行分类的有效性。我们证明了有序杂质和有序两种方法对于分类有序响应数据具有理想的性质，并且与其他先前描述的方法相比都表现良好。开发多基因分类器是微阵列研究的共同目标，因此在高通量甲基化数据集上演示了顺序集成方法的应用。
Many investigators conducting translational research are performing high-throughput genomic experiments and then developing multigenic classifiers using the resulting high-dimensional dataset. In a large number of applications, the class to be predicted may be inherently ordinal. Examples of ordinal outcomes include Tumor-Node-Metastasis (TNM) stage (I, II, III, IV); drug toxicity evaluated as none, mild, moderate, or severe; and response to treatment classified as complete response, partial response, stable disease, or progressive disease. While one can apply nominal response classification methods to ordinal response data, in so doing some information is lost that may improve the predictive performance of the classifier. This study examined the effectiveness of alternative ordinal splitting functions combined with bootstrap aggregation for classifying an ordinal response. We demonstrate that the ordinal impurity and ordered twoing methods have desirable properties for classifying ordinal response data and both perform well in comparison to other previously described methods. Developing a multigenic classifier is a common goal for microarray studies, and therefore application of the ordinal ensemble methods is demonstrated on a high-throughput methylation dataset.
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影响因子：
7.5
作者：
Breiman, L
通讯作者：
Breiman, L
影响因子：
7.5
作者：
Hothorn, T;Lausen, B
通讯作者：
Lausen, B
影响因子：
1.5
作者：
Piccarreta, R
通讯作者：
Piccarreta, R
DOI：
10.1021/ci034160g
发表时间：
2003-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Svetnik, V;Liaw, A;Feuston, BP
通讯作者：
Feuston, BP
影响因子：
30.8
作者：
Blewitt, Marnie E.;Gendrel, Anne-Valerie;Whitelaw, Emma
通讯作者：
Whitelaw, Emma