...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >DNA Copy Number Selection Using Robust Structured Sparsity-Inducing Norms
【24h】

DNA Copy Number Selection Using Robust Structured Sparsity-Inducing Norms

机译：使用健壮的稀疏诱导规范选择DNA拷贝数

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Array comparative genomic hybridization (aCGH) is a newly introduced method for the detection of copy number abnormalities associated with human diseases with special focus on cancer. Specific patterns in DNA copy number variations (CNVs) can be associated with certain disease types and can facilitate prognosis and progress monitoring of the disease. Machine learning techniques have been used to model the problem of tissue typing as a classification problem. Feature selection is an important part of the classification process, because many biological features are not related to the diseases and confuse the classification tasks. Multiple feature selection methods have been proposed in the different domains where classification has been applied. In this work, we will present a new feature selection method based on structured sparsity-inducing norms to identify the informative aCGH biomarkers which can help us classify different disease subtypes. To validate the performance of the proposed method, we experimentally compare it with existing feature selection methods on four publicly available aCGH data sets. In all empirical results, the proposed sparse learning based feature selection method consistently outperforms other related approaches. More important, we carefully investigate the aCGH biomarkers selected by our method, and the biological evidences in literature strongly support our results.
机译：阵列比较基因组杂交（aCGH）是一种新近引入的方法，用于检测与人类疾病相关的拷贝数异常，特别是癌症。 DNA拷贝数变异（CNV）中的特定模式可能与某些疾病类型相关，并且可以促进疾病的预后和进展监测。机器学习技术已被用于将组织类型化问题建模为分类问题。特征选择是分类过程的重要组成部分，因为许多生物学特征与疾病无关，并且混淆了分类任务。在已经应用分类的不同领域中已经提出了多种特征选择方法。在这项工作中，我们将提出一种基于结构性稀疏性诱导准则的新特征选择方法，以识别信息丰富的aCGH生物标志物，这有助于我们对不同疾病亚型进行分类。为了验证所提出方法的性能，我们在四个可公开获得的aCGH数据集上将其与现有特征选择方法进行了实验比较。在所有的经验结果中，所提出的基于稀疏学习的特征选择方法始终优于其他相关方法。更重要的是，我们仔细研究了通过我们的方法选择的aCGH生物标志物，文献中的生物学证据强烈支持了我们的结果。

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