...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Genomics >Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration
【24h】

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration

机译：从DNA甲基化和拷贝数变异估算基因表达：多OMICS集成的深度学习回归模型

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

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.
机译：基因表达分析对提供癌症的分子见解起着重要作用。各种遗传和表观遗传因素（在多ommics下处理）影响基因表达引起癌症表型。最近对多OMIC的理解增长似乎为跨学科生物学的一体化提供资源，因为它们完全可以在癌症中汲取有机体的发育和疾病生物学的综合照片。这种大规模的多OMIC数据可以从公共联盟获得，如癌症基因组Atlas（TCGA）和其他几个平台。由于使用的平台的高噪声和灵敏度，整合来自各种平台的这些多OMICS数据仍然挑战。目前，缺乏抑制来自这些遗传和表观遗传数据的基因表达的稳健的整合预测模型。在这项研究中，我们开发了一种使用深度去噪自动编码器（DDAE）和多层的Perceptron（MLP）制定了基于深度学习的预测模型，其可以定量捕获遗传和表观遗传的改变如何与肝脏肝细胞癌的基因表达的方向性相关（LIHC）。该研究中使用的DDAE已经训练，以从输入OMIC数据中提取显着的特征以估计基因表达。然后，这些特征已被多层Perceptron用于回归和分类任务的反向传播学习。我们已经将拟议的模型与最先进的回归模型进行了基准。最后，已经评估了基于深度学习的集成模型的疾病分类能力，其中获得了95.1％的准确性。

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