• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of the Brazilian Chemical Society >Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry
【24h】

Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry

机译：基于质谱的主成分分析与线性和二次判别分析用于癌症样品识别

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

摘要

Mass spectrometry (MS) is a powerful technique that can provide the biochemical signature of a wide range of biological materials such as cells and biofluids. However, MS data usually has a large range of variables which may lead to difficulties in discriminatory analysis and may require high computational cost. In this paper, principal component analysis with linear discriminant analysis (PCA-LDA) and quadratic discriminant analysis (PCA-QDA) were applied for discrimination between healthy control and cancer samples (ovarian and prostate cancer) based on MS data sets. In addition, an identification of prostate cancer subtypes was performed. The results obtained herein were very satisfactory, especially for PCA-QDA. Selectivity and specificity were found in a range of 90-100%, being equal or superior to support vector machines (SVM)-based algorithms. These techniques provided reliable identification of cancer samples which may lead to fast and less-invasive clinical procedures.
机译：质谱（MS）是一项强大的技术，可为多种生物材料（例如细胞和生物流体）提供生化特征。但是，MS数据通常具有较大范围的变量，这可能导致歧视性分析困难，并可能需要较高的计算成本。本文采用线性判别分析（PCA-LDA）和二次判别分析（PCA-QDA）进行主成分分析，以基于MS数据集区分健康对照和癌症样本（卵巢癌和前列腺癌）。另外，进行了前列腺癌亚型的鉴定。本文获得的结果非常令人满意，尤其是对于PCA-QDA。发现选择性和特异性在90-100％的范围内，等于或优于基于支持向量机（SVM）的算法。这些技术提供了对癌症样品的可靠鉴定，这可能导致快速而侵入性较小的临床程序。

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