.png){kind=icon}
返回
A Top-r Feature Selection Algorithm for Microarray Gene Expression Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBB.2011.151.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides genes into subsets, the sizes of which are relatively small (roughly of size h), then selects informative smaller subsets of genes (of size r < h) from a subset and merges the chosen genes with another gene subset (of size r) to update the gene subset. We repeat this process until all subsets are merged into one informative subset. We illustrate the effectiveness of the proposed algorithm by analyzing three distinct gene expression data sets. Our method shows promising classification accuracy for all the test data sets. We also show the relevance of the selected genes in terms of their biological functions.
Keyword:
Feature selection
classification accuracy
top-r features
DNA microarray gene expression data
期刊
I
IF:
3.4
论文数:
3.3K
被引数:
6.4K
机构
引用论文
Inhibitory effect of tanshinones on rat CYP3A2 and CYP2C11 activity and its structure-activity relationship.png)
Fitoterapia
FitoterapiaIF0
PREAI
分享收藏Long-term acyclovir for prevention of varicella zoster virus disease after allogeneic hematopoietic cell transplantation—a randomized double-blind placebo-controlled studyBlood
OAAI
BloodIF0
OAAI分享收藏MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemiaNATURE GENETICS
NATURE GENETICSIF31.8
PREAI
分享收藏
查看更多内容