Integration of deep learning methods in the classification of rna-seq data

Cervical cancer is a type of gynecological cancer that affects the cells in the lower section of the uterus called the cervix. Cells become abnormal as a result of gene mutations, and uncontrolled division is the primary cause of cancer. On the other hand, Alzheimer's disease is an irreversible neurological disease that causes memory loss and dementia, primarily as a result of brain cell death as people age. The parts of the brain that control thinking, learning, and memory have been injured or destroyed, causing symptoms. Cervical cancer and Alzheimer's disease are both genetic disorders. As a matter of fact, gene expression is significant in the diagnosis and classification of Cervical Cancer and Alzheimer's disease. Many genes' information is kept in RNA-Seq data. To accelerate this approach and assist clinicians in the diagnosis process, methodologies can be constructed using classification algorithms with decreasing the number of irrelevant genes. The objective of this thesis is to use statistical and deep learning approaches to examine Cervical Cancer and Alzheimer's Disease utilizing RNA-Seq datasets built with genes obtained from real samples. To lower the size of the RNA-Seq data set, gene selection is done by 5%, 10%, and 30% among all genes for both datasets, and the gene expression data were generated for each gene according to the importance level of the genes. In three scenarios, the selected genes are trained and tested in the categorization process. Deep Neural networks, convolutional neural networks, and long short-term memory approaches are implemented for classification. Following this research, it will be evaluated which approaches work best in the classifications of Alzheimer's Disease and Cervical Cancer.