Identification of differential gene expression for microarray data using recursive random forestOACSTPCD
Identification of differential gene expression for microarray data using recursive random forest
Background The major difficulty in the research of DNA microarray data is the large number of genes compared with the relatively small number of samples as well as the complex data structure. Random forest has received much a…查看全部>>
Background The major difficulty in the research of DNA microarray data is the large number of genes compared with the relatively small number of samples as well as the complex data structure. Random forest has received much attention recently; its primary characteristic is that it can form a classification model from the data with high dimensionality. However, optimal results can not be obtained for gene selection since it is still affected by undifferentiat…查看全部>>
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Department of Biostatistics, College of Public Health, Harbin Medical University, Harbin, Heilongjiang 150081,China;Department of Biostatistics, College of Public Health, Harbin Medical University, Harbin, Heilongjiang 150081,China;Department of Biostatistics, College of Public Health, Harbin Medical University, Harbin, Heilongjiang 150081,China
医药卫生
microarraygene selectionrecursive random forest
microarraygene selectionrecursive random forest
《中华医学杂志（英文版）》 2008 (24)
基于小波变换理论的基因表达谱分析方法的研究
2492-2496,5
The project was supported by a grant from the National Natural Science Foundation of China (No. 30371253).
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