.png){kind=icon}
.png){kind=icon}
Return
Towards Unsupervised Gene Selection: A Matrix Factorization Framework
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBB.2016.2591545.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The recent development of microarray gene expression techniques have made it possible to offer phenotype classification of many diseases. However, in gene expression data analysis, each sample is represented by quite a large number of genes, and many of them are redundant or insignificant to clarify the disease problem. Therefore, how to efficiently select the most useful genes has been becoming one of the most hot research topics in the gene expression data analysis. In this paper, a novel unsupervised twostage coarse-fine gene selection method is proposed. In the first stage, we apply the kmeans algorithm to over-cluster the genes and discard some redundant genes. In the second stage, we select the most representative genes from the remaining ones based on matrix factorization. Finally the experimental results on several data sets are presented to show the effectiveness of our method.
Keywords:
Gene selection
matrix factorization
Journal
I
IF:
3.4
Papers:
3.3K
Citations:
6.4K