.png){kind=icon}
返回
Gene subset selection in microarray data using entropic filtering for cancer classification
.png){kind=icon}
PRE
AI
DOI：10.1111/j.1468-0394.2008.00489.x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this work an entropic filtering algorithm (EFA) for feature selection is described, as a workable method to generate a relevant subset of genes. This is a fast feature selection method based on finding feature subsets that jointly maximize the normalized multivariate conditional entropy with respect to the classification ability of tumours. The EFA is tested in combination with several machine learning algorithms on five public domain microarray data sets. It is found that this combination offers subsets yielding similar or much better accuracies than using the full set of genes. The solutions obtained are of comparable quality to previous results, but they are obtained in a maximum of half an hour computing time and use a very low number of genes.
Keyword:
microarray gene expression
feature selection
cancer classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.3
论文数:
2.6K
被引数:
3.8K
机构
引用论文
Local structural change in GaCrN grown by radio frequency plasma-assisted molecular-beam epitaxy
PREAI
分享收藏RAGE activation by S100P in colon cancer stimulates growth, migration, and cell signaling pathways
PREAI
分享收藏Isolation and characterization of e3B1, an eps8 binding protein that regulates cell growth.png)
ONCOGENE
ONCOGENEIF7.3
PREAI
分享收藏分享收藏Development of a reverse capture autoantibody microarray for studies of antigen-autoantibody profilingPROTEOMICS
PROTEOMICSIF3.9
PREAI
分享收藏
查看更多内容