A performance analysis of clustering based algorithms for the microarray gene expression data

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Current advancements in microarray technology permit simultaneous observing of the expression levels of huge number of genes over various time points. Microarrays have obtained amazing implication in the field of bioinformatics. It includes an ordered set of huge different Deoxyribonucleic Acid (DNA) sequences that can be used to measure both DNA as well as Ribonucleic Acid (RNA) dissimilarities. The Gene Expression (GE) summary aids in understanding the basic cause of gene activities, the growth of genes, determining recent disorders like cancer and as well analysing their molecular pharmacology. Clustering is a significant tool applied for analyzing such microarray gene expression data.  It has developed into a greatest part of gene expression analysis. Grouping the genes having identical expression patterns is known as gene clustering. A number of clustering algorithms have been applied for the analysis of microarray gene expression data. The aim of this paper is to analyze the precision level of the microarray data by using various clustering algorithms.

     


  • Keywords


    Microarray Technology, Gene Expression Data, Clustering Algorithms.

  • References


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Article ID: 12172
 
DOI: 10.14419/ijet.v7i2.21.12172




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