Microarray gene expression data analysis with data mining methods

In parallel with the accumulation of information obtained from the human genome project, microarray technology has also developed. With this technology, progress has been made especially in the functions of genes and regulatory mechanisms and determining in the genome. Data mining methods have become the most important suppertive to the researchers at this point. The most important reason of that is the lack of use of the methods of classical statistics due to certain assumptions (normal distribution, homogenity of variances) in analyzing the microarray data sets. Data mining methods on the other hand conclude the analyzes correctly almost requiring no assumption. The aim of this study is to introduce the main flow chart in the analyzing of gene expressing data. These are dimension reduction, selecting the method of generalization, supervisedunsupervised methods, performance criteria and gene ontology, in order. By this study, alternative methods and resources, which will help the scientists who work in genetic researches in our country in the analyzing of data, have been introduced together.