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Application of the GA/KNN method to SELDI proteomics data

机译:GA / KNN方法在SELDI蛋白质组学数据中的应用

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摘要

Proteomics technology has shown promise in identifying biomarkers for disease, toxicant exposure and stress. We show by example that the genetic algorithm/k-nearest neighbors method, developed for mining high-dimensional microarray gene expression data, is also capable of mining surface enhanced laser desorption/ionization–time-of-flight proteomics data.
机译:蛋白质组学技术在识别疾病,有毒物质暴露和压力的生物标志物方面显示出了希望。我们通过示例显示,为挖掘高维微阵列基因表达数据而开发的遗传算法/ k最近邻方法也能够挖掘表面增强的激光解吸/电离-飞行时间蛋白质组学数据。

著录项

  • 来源
    《Bioinformatics》 |2004年第10期|p. 1638-1640|共3页
  • 作者单位

    Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA;

    Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA;

    Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA;

    Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物科学;
  • 关键词

  • 入库时间 2022-08-17 23:50:21

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