Year: 2014
Type: article
Abstract: Currently, there are two techniques used for large-scale gene-expression profiling; microarray and RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used in microarray data analysis.Microarray is a DNA molecules array which allows multiple hybridization experiments to be carried out simultaneously and trace expression levels of thousands of genes.It is a highthroughput technology for gene expression analysis and becomes an effective tool for biomedical research.Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein microarrays, which enable researchers to investigate the expression state of a large number of genes.Data clustering represents the first and main process in microarray data analysis.The k-means, fuzzy c-mean, selforganizing map, and hierarchical clustering algorithms are under investigation in this paper.These algorithms are compared based on their clustering model.
Institution: Universiti Putra Malaysia
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W2098805999