# Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification
## 1 Introduction  
Cancer is a disease that starts with abnormal behavior and division of some cells, causing damage to other, nearby cells, resulting in a clot or tumor which, in certain cases, may cause death [1]. Early discovery and proper treatment can reduce the chances of damage to other cells. The high mortality rate from cancer [2] is motivating researchers to develop new methods for early cancer detection and classification. However, early detection is very complicated, because cancer cells are disordered. RNA-Seq analysis is extremely helpful in this regard. RNA-Seq is a new and popular technique that is used to detect new isoforms and transcripts by providing more normalized and less noisy data for prediction and classification purposes [3, 4]. The most important function of transcriptome profiling is to determine the differentially expressed genes occurring in a body or detect variations in genes at different levels [5]. Identification and quantification in one place can be made using RNA-sequencing [6]. RNA-Seq data are widely available from different databases, and arebeing used to classify diseases like breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), kidney chromophobe, etc. [7]. However, analyses of RNA gene expression data are quite complex because of their high dimensions, complexity, and the existence of duplications in feature values [8]. Therefore, a need for automatic feature extraction exists, which may be addressed through machine learning (ML) and deep learning algorithms [9]. Machine learning is a branch of artificial intelligence which is used to identify associations among data by finding underlying patterns using past experience and learning [10]. ML is becoming indispensable in the age of mass data, given that it is becoming increasingly difficult for humans to find trends and patterns in data to predict future outcomes. Hence, machine learning is replacing humans to identify underlying patterns in data and make predictions for the future to make proper decisions. ML extracts features itself with almost zero human intervention, and then uses these features to make predictions. ML is being implemented almost everywhere. Its typical applications are in natural language processing, forecasting, flight governance, and biology to detect sequences of proteins and RNA [11; 12]. There are certain limitations of ML-based algorithms in terms of selecting promising features from biomedical images for classification. However, these limitations are being overcome by deep learning. Deep learning is an emerging field based upon some advancements in ML. It is a technique which, without considering the in-between steps of feature extraction, tries to focus on making a conclusion based on raw data. This is why it is also named \"automated feature engineering\" [13; 14]. Deep learning is being actively used in many research areas, including bioinformatics, computation medicine, image and graphical information processing, etc. [7]. A convolutional neural network is a DL model for use with a large number of graphical images. Using weighted distribution, subsampling, and confined association techniques, CNN extracts the most relevant features and reduces the complexity of the neural network [15]. Deep learning is being implemented in many disease identification processes, and is improving machine learning performance in the field [16]. Multilayer perceptron (MLP) is a modern technology known as a feed-forward neural network used in deep learning to identify and classify different types of tumors [17; 18]. A previous study lists instances in which deep learning has been used as stacked denoising autoencoders (SDAE) to transfer high dimensional noisy data to low dimensional data for the classification of breast cancer [8]. Another study proposed and implemented a new approach named convolutional neural network for coexpression (CNNC), and the task of gene relationship inference in a supervised setting was performed [19; 20; 21].