New gene selection method for classification of cancer subtypes considering within-class variation
New gene selection method for classification of cancer subtypes considering within-class variation
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DOI：
10.1016/s0014-5793(03)00819-6
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发表时间：
2003-09-11
期刊：
影响因子：
3.5
通讯作者：
Lee, IB
中科院分区：
文献类型：
--
作者：
Cho, JH;Lee, D;Lee, IB
在这项工作中，我们提出了一种新的方法来寻找基因子集的微阵列数据，有效地区分疾病的亚型。我们开发了一种新的标准，用于测量个体基因的相关性，通过使用从每个样本到类质心的距离的平均值和标准差，以治疗众所周知的基因选择问题，大类内变异。此外，这种方法的优点是，它不仅适用于二进制分类，但也适用于多个分类问题。我们通过将其应用于公开可用的微阵列数据集，白血病（两类）和小圆蓝细胞肿瘤（四类），证明了该方法的性能。与以往的方法相比，该方法提供了非常少的基因，而不损失区分能力，因此它可以有效地促进进一步的生物学和临床研究。(C)2003年欧洲生物化学学会联合会。Elsevier B.V.出版，保留所有权利。
In this work we propose a new method for finding gene subsets of microarray data that effectively discriminates subtypes of disease. We developed a new criterion for measuring the relevance of individual genes by using mean and standard deviation of distances from each sample to the class centroid in order to treat the well-known problem of gene selection, large within-class variation. Also this approach has the advantage that it is applicable not only to binary classification but also to multiple classification problems. We demonstrated the performance of the method by applying it to the publicly available microarray datasets, leukemia (two classes) and small round blue cell tumors (four classes). The proposed method provides a very small number of genes compared with the previous methods without loss of discriminating power and thus it can effectively facilitate further biological and clinical researches. (C) 2003 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.