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An Approach to Analyzing LOH Data of Lung Cancer Based on Biclustering and GA

机译:基于双聚类和遗传算法的肺癌LOH数据分析方法

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摘要

There is a close relation between the phenomenon of LOH and malignant tumor. Bicluster algorithms have been applied to the data of loss of heterozygosity analysis and can find the submatrix which is composed by SNPs loci related to cancer. But the conventional Cheng and Church method requires experience values as a threshold, and discovered results must be randomized. In this paper, we use k-means and GA to overcome this shortcoming. The experimental results demonstrate the effectiveness and accuracy of our method in discovering chromosome segments related to suppressor genes of lung cancer.
机译:LOH现象与恶性肿瘤之间有密切的关系。 Bicluster算法已应用于杂合性缺失分析数据,并且可以找到由与癌症相关的SNP位点组成的子矩阵。但是传统的Cheng and Church方法需要经验值作为阈值,并且发现的结果必须随机化。在本文中,我们使用k-means和GA来克服这一缺点。实验结果证明了我们的方法在发现与肺癌抑制基因相关的染色体片段方面的有效性和准确性。

著录项

  • 来源
  • 会议地点 Shanghai(CN);Shanghai(CN)
  • 作者单位

    School of Computer Engineering Science Shanghai University 200072 Shanghai, China;

    School of Computer Engineering Science Shanghai University 200072 Shanghai, China;

    School of Computer Engineering Science Shanghai University 200072 Shanghai, China;

    School of Computer Engineering Science Shanghai University 200072 Shanghai, China;

    School of Computer Engineering Science Shanghai University 200072 Shanghai, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
  • 关键词

    LOH; tumor; SNPs; biclustering;

    机译:H瘤; SNP;双集群;
  • 入库时间 2022-08-26 14:08:04

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