THE NEW HYBRID METHOD FOR CLASSIFICATION OF PATIENTS BY GENE EXPRESSION PROFILING

Article type: Research Article

Authors: Cosgun, Erdal | Karaagaoglu, Ergun

Affiliations: Department of Biostatistics, Faculty of Medicine, Hacettepe University, Ankara, Turkey

Abstract: Genetic researches have gradually become an area which is intensively studied on in recent years. The reason of that is the fact that a lot of diseases and features are transferred to the other generations by genes. These transfers are generally at the base of diseases. The evaluation of the input which is reached as the result of the researches is also accepted as a separate field. The aim of this study is to develop a model which enables the best classification of the patients by DNA microarray expression inputs. For this purpose, the classification which is based on Unsupervised Learning has mainly been used, by bringing together various methods. The Independent Components Analysis is used for dimension reduction, Kohonen Map Method is used for clustering and Random Forest Method is used for classification purposes. The model which is formed by combining these methods and very popular classification method Support Vector Machines (SVMs) has been studied and their classification performance is compared by True Classification Rate (TCR) on two real publicity data sets. The highest value that TCR can take on is one. The aim is to close this value to one. By the help of the model proposed in this study, we expect a reduction in the cost of these researches and aim to prevent wrong diagnoses as much as possible.

Keywords: data mining, random forest, independent component analysis, Kohonen map, bootstrap, classification, clustering, dimension reduction, microarray data

Journal: Journal of Integrated Design & Process Science, vol. 14, no. 2, pp. 27-42, 2010