# Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification
## 2 Related Work  
[PERSON] et al. [23] used different machine learning algorithms to classify cancer. In their study, a dataset was downloaded from an online data portal, COSMIC, for multiple cancer types. The applied machine learning models were random forest (RF), gradient boosting machine (GBM), neural networks (NN), K nearest neighbor (KNN), and support vector machine (SVM). The authors performed multiple experiments for various cancer types and primary sites. Notably, RF achieved 100% accuracy in classification and was easy to tune compared to other algorithms. [PERSON] et al. [24] proposed a new deep learning-based, multimodel ensemble approach that uses five machine learning algorithms, i.e., KNN, SVM, DT's, RF's, and gradient boosting decision trees (GBDT). Their proposed strategy was applied to three types of cancer: LUAD, stomach adenocarcinoma (STAD), and BRCA. This strategy was implemented so that each classifier would be trained using the provided data to obtain predictions individually; these predictions are then applied to a multimodel ensemble approach using deep learning. This method provides more accurate results compared to those generated by an individual classifier to predict cancer. [PERSON] et al. [25] presented a new range of classifiers based on Voom, named \"voomNSC\", \"voomNBLDA\", \"voomPLDA\", as well as SVM classifiers for the classification and evaluation of RNA-Sequencing data on cervical and lung cancer, as well as aging datasets. VoomNSC is based on voom transformation with the NSC method to build more accurate and robust classifiers. VoomDLDA and voomDQDA are not sparse base, which means that they use all of the features provided in the model. In contrast, voomNSC is a sparse base classifier and uses the only subset of features in the model. The results were compared with PLDA, NBLDA, NSC, and it was found that voomNSC produced the best results. [PERSON] et al. [26] presented a novel numerical approach for CoRAL (classification of RNA by analysis of length). For this purpose, the authors took small RNA sequence datasets and sequenced them. Then, multiple preprocessing steps were performed, i.e., the dataset was passed to three trimmed adapter sequences, and a FASTQ file was generated. By matching with a reference file, reads were aligned, and results were stored in a SAM file. After this, the authors executed a mismatch rate on reads, and again, the results were added to a SAM file. After these steps, aligned matched genes were converted to a BAM file to be presented to CoRAL. CoRAL extracts important features and classifies multiple types of RNA sequences. This method not only classifies small RNA sequences, but also provides better guidance to the user. [PERSON] et al. [27] proposed a novel optimized deep learning approach based on binary particle swarm optimization-decision tree (BPSO--DT) and CNN. The dataset was used in their study to classify different types of cancer, i.e., kidney renal clear cell carcinoma (KIRC), BRCA, lung squamous cell carcinoma (LUSC), lung adenocarcinoma(LUAD), and uterine corpus endometrial carcinoma(UCEC). This approach comprised three phases. The first was related to feature extraction, and BPSO was used to extract relevant features. The second phase aimed to solve the problem of overfitting data to get accurate results, and as such, was called the augmentation phase. The third and last phase was the deep CNN phase, which was used the CNN architecture of connected layers to classify types of cancer based on given data. This methodology produced more accurate results than the CNN technique. [PERSON] et al. [28] put forward a new pipeline approach for predicting the survival chances of cancer patients. The proposed technique used graph-based semisupervised learning Laplacian support vector machines. This approach was used to predict the survival of kidney cancer (KIRC) and neuroblastoma (NB) patients. It comprised four steps. The first is preprocessing, in which data are analyzed and stored in feature metrics.