د. سعيد محمود زميط2025-03-192025-03-19https://dspace.academy.edu.ly/handle/123456789/1561Over the last few years, many studies have focused on the classification and diagnosis of breast cancer using Microarray Technology. Several methods are used to analyze microarray data, yet R programming language is one of the most commonly used tools, R is an open source and open development software project for the analysis and comprehension of genomic data, based on the R. In this study, we analyzed secondary microarray data of 6 healthy samples and 4 breast cancer samples using R and Bioconductor software. The data was downloaded as CEL files from Gene Expression Omnibus (GEO) data sets through the NCBI website. The files firstly imported, read, preprocessed and assessed in R to insure its quality before the actual analysis. Differential expression profiles obtained utilizing the linear model (LIMMA) package. The resulting calculations was then clustered and used to produce heatmapOver the last few years, many studies have focused on the classification and diagnosis of breast cancer using Microarray Technology. Several methods are used to analyze microarray data, yet R programming language is one of the most commonly used tools, R is an open source and open development software project for the analysis and comprehension of genomic data, based on the R. In this study, we analyzed secondary microarray data of 6 healthy samples and 4 breast cancer samples using R and Bioconductor software. The data was downloaded as CEL files from Gene Expression Omnibus (GEO) data sets through the NCBI website. The files firstly imported, read, preprocessed and assessed in R to insure its quality before the actual analysis. Differential expression profiles obtained utilizing the linear model (LIMMA) package. The resulting calculations was then clustered and used to produce heatmapEarly Diagnosis of Breast Cancer Using Microarray Technology