NDLI: FCM-SVM-RFE Gene Feature Selection Algorithm for Leukemia Classification from Microarray Gene Expression Data

Content Provider	IEEE Xplore Digital Library
Author	Yuchun Tang Yan-Qing Zhang Zhen Huang
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA (Yuchun Tang; Yan-Qing Zhang)
Abstract	Selecting the most possibly cancer-related genes from huge microarray gene expression data is an important bioinformatics research topic due to its significance to improve human's understandability of the inherent cancer-resulting mechanism. This is actually a feature selection problem. The huge number of genes makes it impossible to execute an exhaustive search. In this work, we propose a recursive feature elimination (RFE) algorithm named FCM-SVM-RFE for the gene selection task. In each step, similar genes are grouped into clusters by the fuzzy C-means clustering algorithm, and then a support vector machine (SVM) is modeled in each cluster-induced space, the genes which contribute large to the margin width of the SVM are selected to survive to the next step. This process is repeated until a pre-specified number of genes are selected. FCM-SVM-RFE is compared with SVM-RFE on AML/ALL microarray gene expression data. The experimental results show that FCM-SVM-RFE is more accurate than SVM-RFE to predict the unknown samples. More importantly, FCM-SVM-RFE can find some compact subsets of genes on each of which a SVM with perfect prediction accuracy can be modeled. These "most informative genes" are very helpful for biologists to efficiently and effectively find the inherent cancer-resulting mechanism
Sponsorship	IEEE IEEE Neural Networks Soc
Starting Page	97
Ending Page	101
File Size	1892700
Page Count	5
File Format	PDF
ISBN	0780391594
DOI	10.1109/FUZZY.2005.1452375
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification algorithms Gene expression Support vector machines Bioinformatics Clustering algorithms Data mining Accuracy Biological system modeling Predictive models Data analysis
Content Type	Text
Resource Type	Article

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