...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>BMC Bioinformatics >Deep gene selection method to select genes from microarray datasets for cancer classification
【24h】

Deep gene selection method to select genes from microarray datasets for cancer classification

机译：深基因选择方法从微阵列数据集中选择基因进行癌症分类

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

BACKGROUND:Microarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis.RESULTS:The gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost.CONCLUSIONS:We provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples' classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.
机译：背景：微阵列数据集由复杂和高维样本和基因组成，并且通常样品的数量远小于基因的数量。由于这种数据不平衡，基因选择是微阵列表达数据分析的苛刻任务。结果：DGS选择的基因集显示了其在癌症分类中的优异性能。 DGS具有降低原始微阵列数据集中的基因数量的高能力。与其他代表性和最先进的基因选择方法的实验比较也表明，DG在所选基因，分类准确性和计算成本的数量方面实现了最佳性能。结论：我们提供了一种有效的基因选择算法可以选择对样品类显着敏感的相关基因。随着若干鉴别基因和较少的成本时间通过所提出的算法在几个公共微阵列数据上实现了很大的预测准确性，这反过来验证了所提出的基因选择方法的效率和有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号