Comparison of classification performance for deep learning method and classical data mining methods on transcriptome data set

In this thesis, Artificial Neural Networks, Random Forest, Support Vector Machines, which are classical data mining methods, and Deep Learning method were used to classify the cancer subtypes. The performances of these methods were compared. For this reason, two different RNA sequencing data sets were used. The first data set is the lung cancer data set which has two classes. It is a balanced data set in terms of class size. The other data set is the renal cancer data set. This data set contains three classes and the number of observation in these classes are uneven. Gene sets used in the classification were obtained by using different filters. Therefore the performances of the classification methods in different data sets and filters were examined. For each classification method, specific parameters were optimized and the most appropriate parameters were selected. Deep Learning method which has a deeper structure compared to classical data mining methods, showed a successful performance on the data sets used in this study. Key Words: RNA sequencing, cancer, data mining, classification methods, Deep Learning