Gene Selection Using High Dimensional Gene Expression Data: An Appraisal

Title:Gene Selection Using High Dimensional Gene Expression Data: An Appraisal

Volume: 13 Issue: 3

Author(s): Abhishek Bhola*Shailendra Singh*

Affiliation:

• Department of Computer Science and Engineering, PEC University of Technology, Chandigarh (160012),India

• Department of Computer Science and Engineering, PEC University of Technology, Chandigarh (160012),India

Keywords: Gene selection, gene expression data, feature extraction, high dimensionality, dimensionality reduction, microarray.

Abstract: Microarray technology allows us to study the gene expression levels of thousands of genes over different experimental conditions in a single go. The gene expression data provided by microarray technology is of enormous size i.e. high dimensional which makes the downstream analysis a very challenging task. The gene selection is an essential process which removes the problem of dimensionality by removing irrelevant and unwanted genes from gene expression data. A variety of gene selection techniques are available in the literature which are used widely to find the most informative and significant genes from the given gene expression dataset. This paper reviews different aspects of gene selection and other research issues which came across while analyzing gene expression data. The article provides a brief overview of the gene selection for high dimensionality reduction in gene expression data.

Cite this article as:

Bhola Abhishek *, Singh Shailendra *, Gene Selection Using High Dimensional Gene Expression Data: An Appraisal, Current Bioinformatics 2018; 13 (3) . https://dx.doi.org/10.2174/1574893611666160610104946