...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Biclustering of expression data with evolutionary computation
【24h】

Biclustering of expression data with evolutionary computation

机译：通过进化计算将表达数据分类

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

Microarray techniques are leading to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In this work, we address the biclustering of gene expression data with evolutionary computation. Our approach is based on evolutionary algorithms, which have been proven to have excellent performance on complex problems, and searches for biclusters following a sequential covering strategy. The goal is to find biclusters of maximum size with mean squared residue lower than a given /spl delta/. In addition, we pay special attention to the fact of looking for high-quality biclusters with large variation, i.e., with a relatively high row variance, and with a low level of overlapping among biclusters. The quality of biclusters found by our evolutionary approach is discussed and the results are compared to those reported by Cheng and Church, and Yang et al. In general, our approach, named SEBI, shows an excellent performance at finding patterns in gene expression data.
机译：微阵列技术正在导致能够从生物医学的角度提取新颖有用的知识的复杂算法的发展。在这项工作中，我们通过进化计算解决了基因表达数据的聚类问题。我们的方法基于进化算法，该算法已被证明在复杂问题上具有出色的性能，并遵循顺序覆盖策略搜索双聚类。目标是找到最大大小的双峰，均方差小于给定的/ spl delta /。另外，我们特别注意以下事实：寻找具有较大变化的高质量双曲线，即行变化较大，并且双曲线之间的重叠程度较低。讨论了通过我们的进化方法发现的双簇的质量，并将结果与​​Cheng和Church和Yang等报道的相比较。总的来说，我们的名为SEBI的方法在发现基因表达数据中的模式方面表现出出色的性能。

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