Paper Info
Title
Selecting Informative Genes from Microarray Dataset Using Fuzzy Relational Clustering
Abstract
Selecting informative genes from microarray experiments is one of the most important data analysis steps for deciphering biological information imbedded in such experiments. This paper presents a novel approach for selecting informative genes in two steps. First, fuzzy relational clustering is used to cluster co-expressed genes and select genes that express differently in distinct sample conditions. Second, Support Vector Machine Recursive Feature Elimination (SVM-RFE) method is applied to rank genes. The proposed method is tested on cancer datasets for cancer classification. The results show that the proposed feature selection method selects better subset of genes than the original SVM-RFE does and improves the classification accuracy.
Year
DOI
Venue
2008
10.1007/978-3-540-89985-3_102
ADVANCES IN COMPUTER SCIENCE AND ENGINEERING
Keywords
DocType
Volume
Gene selection,microarray data,redundancy reduction
Conference
6
ISSN
Citations 
PageRank 
1865-0929
0
0.34
References 
Authors
7
2
Name
Order
Citations
PageRank
Soudeh Kasiri-Bidhendi171.17
Saeed Shiry Ghidary216120.22