• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction
【24h】

Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction

机译：与深层子空间融合聚类集成多OMIC数据，用于癌症亚型预测

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

One type of cancer usually consists of several subtypes with distinct clinical implications, thus the cancer subtype prediction is an important task in disease diagnosis and therapy. Utilizing one type of data from molecular layers in biological system to predict is difficult to bridge the cancer genome to cancer phenotypes, since the genome is neither simple nor independent but rather complicated and dysregulated from multiple molecular mechanisms. Similarity Network Fusion (SNF) has been recently proposed to integrate diverse omics data for improving the understanding of tumorigenesis. SNF adopts Euclidean distance to measure the similarity between patients, which shows some limitations. In this article, we introduce a novel prediction technique as an extension of SNF, namely Deep Subspace Fusion Clustering (DSFC). DSFC utilizes auto-encoder and data self-expressiveness approaches to guide a deep subspace model, which can achieve effective expression of discriminative similarity between patients. As a result, the dissimilarity between inter-duster is delivered and enhanced compactness of intra-cluster is achieved at the same time. The validity of DSFC is examined by extensive simulations over six different cancer through three levels omics data. The survival analysis demonstrates that DSFC delivers comparable or even better results than many state-of-the-art integrative methods.
机译：一种癌症通常由几种具有不同临床意义的亚型组成，因此癌症亚型预测是疾病诊断和治疗中的重要任务。利用生物系统中的分子层中的一种数据来预测，难以将癌症基因组桥给癌症表型，因为基因组既不是单纯性也不是无关的，而是从多个分子机制中的复杂性和困惑。最近已经提出了相似性网络融合（SNF）来整合各种OMIC数据以改善对肿瘤发生的理解。 SNF采用欧几里德距离来测量患者之间的相似性，这表明了一些局限性。在本文中，我们将一种新颖的预测技术介绍为SNF的延伸，即深层子空间融合聚类（DSFC）。 DSFC利用自动编码器和数据自我表达力方法来指导深度子空间模型，这可以实现患者之间有效表达歧视性相似性的。结果，同时递送了除尘器之间的多样性，同时实现了内部簇的压缩性。通过三级OMIC数据通过六种不同癌症的广泛模拟来检查DSFC的有效性。生存分析表明，DSFC提供比许多最先进的一体化方法相当或甚至更好的结果。

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