A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset
A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset
复制标题
遗传算法和支持向量机的鲁棒混合，用于提取最佳特征基因子集
DOI：
10.1016/j.ygeno.2004.09.007
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发表时间：
2005-01-01
期刊：
影响因子：
4.4
通讯作者：
Rao, S
中科院分区：
文献类型：
--
作者：
Li, LB;Jiang, W;Rao, S
从微阵列数据中提取有用信息的强大而有效的方法的发展仍然是一个重大的和具有挑战性的任务。微阵列数据的特点是高维度、高信噪比和基因间的高相关性，但样本量相对较小。目前的降维方法可以进一步改进，用于存在单个（或几个）高影响力基因的情况，其中其在特征子集中的作用将禁止包含其他重要基因。我们已经形成了一个强大的基因选择方法的基础上的混合遗传算法和支持向量机。这种杂交的主要目标是充分利用它们各自的优点（例如，对解空间大小的鲁棒性和处理非常大维度的特征基因的能力），用于鉴定复杂生物表型的关键特征基因（或分子标记）。我们将该方法应用于弥漫性大B细胞淋巴瘤的微阵列数据，以展示其行为和属性，挖掘全基因组基因表达谱的高维数据。与边缘滤波器和遗传算法与K最近邻之间的混合相比，所得到的分类器（最佳基因子集）已经实现了独立微阵列样品的预测的最高准确度（99%）。(C)2004爱思唯尔公司All rights reserved.
Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data are characterized by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset would prohibit inclusion of other important genes. We have formalized a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine. The major goal of this hybridization was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the microarray data of diffuse large B cell lymphoma to demonstrate its behaviors and properties for mining the high-dimension data of genome-wide gene expression profiles. The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99%) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between genetic algorithm and K nearest neighbors. (C) 2004 Elsevier Inc. All rights reserved.