2016•Unpublished venueRequires access

Clustering Methods Applied for Gene Expression Data: A Study

Shelly R. Gupta, Shailender Narayan Singh, Dharminder Kumar

Open publisher page 9 citations

Abstract

In bioinformatics research, the biggest challenge is to extract information from large datasets according to ones interestingness criteria. For research in genetics the DNA microarray technology provides better results in comparison with the standard approach as it has computerized the parallel analysis of thousands of genes for monitoring expression levels of genes. Thus, for the biologists the challenge to analyze gene data which consists of a huge number of measurements is in the range of clustering techniques. For the above purpose till now various clustering techniques have been developed and applied on gene microarray data. Thus, this study paper is aimed to provide a brief review of the various research papers and journals on recent research done for cluster analysis of gene microarray data.

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What this paper is about

In bioinformatics research, the biggest challenge is to extract information from large datasets according to ones interestingness criteria. For research in genetics the DNA microarray technology provides better results in comparison with the standard approach as it has computerized the parallel analysis of thousands of genes for monitoring expression levels of genes. Thus, for the biologists the challenge to analyze gene data which consists of a huge number of measurements is in the range of clustering techniques. For the above purpose till now various clustering techniques have been developed and applied on gene microarray data. Thus, this study paper is aimed to provide a brief review of the various research papers and journals on recent research done for cluster analysis of gene microarray data.

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OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In bioinformatics research, the biggest challenge is to extract information from large datasets according to ones interestingness criteria. For research in genetics the DNA microarray technology provides better results in comparison with the standard approach as it has computerized the parallel analysis of thousands of genes for monitoring expression levels of genes. Thus, for the biologists the challenge to analyze gene data which consists of a huge number of measurements is in the range of clustering techniques. For the above purpose till now various clustering techniques have been developed and applied on gene microarray data. Thus, this study paper is aimed to provide a brief review of the various research papers and journals on recent research done for cluster analysis of gene microarray data.

Key concepts: Cluster analysis, Microarray analysis techniques, Computer science, Gene chip analysis, Data mining, Microarray databases, DNA microarray, Computational biology

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