Original scientific paper
https://doi.org/10.1080/00051144.2023.2284027
Spatial clustering based gene selection for gene expression analysis in microarray data classification
P. Edwin Dhas
; Department of Computer Science and Engineering, Jayaraj Annapackiam CSI College of Engineering, Nazareth, India
*
S Lalitha
; Dept. of Electronics and Communication Engineering, B.M.S. College of Engineering, Bengaluru, India
Annalakshmi Govindaraj
; Department of CSE, Koneru Lakshmaiah Education Foundation, Hyderabad, India
B. Jyoshna
; Department of CSE, Keshav Memorial Institute of Technology, Hyderabad, India
* Corresponding author.
Abstract
A typical application of categorization in data mining is to uncover interesting distributions
and significant patterns in the information that underlies it using density-based spatial clustering for workloads with noise. In these conditions, it is anticipated that the classification of
the microarray gene expression database will have the necessary clustering property that may
be utilized to emphasize the effects of the alterations. The proposed method typically guarantees that the subsequent identification of gene clusters’ best global arrangement of genes. It
provides an iterative method for figuring out the precise number of clusters needed for each
data collection. The technique is based on practices frequently used in statistical tests. The
key idea is to coordinate gene redistribution optimization across clusters with the search for
the optimal number of groups. An experiment that finds the most effective number of genes
over time was used to evaluate the effectiveness of the suggested strategy. It used this stringent statistical test to show that our technique accurately clusters more than 95% of the genes.
Finally, since the basic principles of gene development and gene cluster assignment have been
well characterized by earlier studies and the technique was verified using real gene expression
information.
Keywords
Gene selection; feature selection; microarray gene expression; spatial cluster optimization algorithm
Hrčak ID:
322957
URI
Publication date:
10.12.2023.
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