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Cited by 1,235

The DOI matches its Crossref record; 131 of the 131 references it cites were found in a registry or an index.

Cluster analysis for gene expression data: a survey

Daxin Jiang, Chun Tang, Aidong Zhang·2004·IEEE Transactions on Knowledge and Data Engineering
DOI 10.1109/tkde.2004.68
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Abstract

DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increases the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. In particular, we divide cluster analysis for gene expression data into three categories. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches. We also discuss the problem of cluster validation in three aspects and review various methods to assess the quality and reliability of clustering results. Finally, we conclude this paper and suggest the promising trends in this field.

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Metadata

Authors
Jiang, D., Tang, C., & Zhang, A.
Venue
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2004-11
Volume
16
Issue
11
Pages
1370-1386
ISSN
1041-4347 (ISSN-L)
DOI
10.1109/tkde.2004.68
OpenAlex
W2141012957
Cited by
1,235
References
131
FWCI
12.90
Type
journal article
Language
en
Open access
closed

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