A comparative study of deep learning methods for classification of rna-seq cancer data

Cancer is one of the most important causes of deaths today. Millions of people die because of cancer every year, while millions of people are diagnosed with cancer. Cancer is a gene disease. As a result of mutations in genes, cells become abnormal and uncontrolled division is the main cause of cancer disease. Therefore, gene expression is very important in the diagnosis and classification of cancer. RNA-Seq data stores information of many genes. Many of these genes found on RNA-Seq data have nothing to do with cancer. Finding which genes cause cancer and then diagnosing the type of cancer is a long time process. Decision support systems can be developed using classification algorithms or deep learning methods to shorten this process and assist doctors in the diagnosis process. The aim of this thesis is to analyze the cancer type using clasical methods, artificial neural networks and deep learning methods by using RNA-Seq datasets created with genes obtained from previously diagnosed cancer patients. First, gene selection is made using wrapper methods to reduce the size of the RNA-Seq data set. The selected genes are then used in the classification process. For classification, decision trees, random forests, support vector machines, artificial neural networks and deep learning methods are used. After this study, which method works better in cancer classifications is examined. The method developed according to the results is expected to help doctors in the process of cancer classification. Keywords: Cancer, Gene Expression, RNA-Seq, Classification, Deep Learning