NDLI: Analyzing the Escherichia coli gene expression data by a multilayer adjusted tree organizing map

Content Provider	IEEE Xplore Digital Library
Author	Ning Wei Le Gruenwald Conway, T.
Copyright Year	2003
Description	Author affiliation: Sch. of Comput. Sci., Oklahoma Univ., USA (Ning Wei; Le Gruenwald)
Abstract	Using the DNA microarray technology, biologists have thousands of array data available. Discovering the function relations between genes and their involvements in biological processes depends on the ability to efficiently process and quantitatively analyze large amounts of array data. Clustering algorithms are among the popular tools that can be used to help biologists achieve their goals. Although some existing research projects employed clustering algorithms on biological data, none of them has examined the Escherichia coli (E. coli) gene expression data. This paper proposes a clustering algorithm called Multilayer Adjusted Tree Organizing Map (MA TOM) to analyze the E. coli gene expression data. In a semi-supervised manner, MATOM constructs a multilayer map, and at the same time, removes noise data in the previously trained maps in order to improve the training process. This paper then presents the clustering results produced by MATOM and other existing clustering algorithms using the E. coli gene expression data, and a new evaluation method to assess them. The results show that MATOM performs the best in terms of percentage of genes that are clustered correctly.
Sponsorship	IEEE Comput. Soc. Inf. Technol. Res. Inst. at Wright State Univ. IEEE Neural Networks Soc
Starting Page	289
Ending Page	296
File Size	250129
Page Count	8
File Format	PDF
ISBN	0769519075
DOI	10.1109/BIBE.2003.1188965
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-03-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene expression Nonhomogeneous media Organizing Clustering algorithms Biological processes DNA Bioinformatics Genomics Computer science Algorithm design and analysis
Content Type	Text
Resource Type	Article

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