Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data* * This paper is an extended version of 11th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2018) Paper (Huynh, Nguyen, & Do, 2018a). In this paper, in addition to ACIIDS 2018 paper, we have been built DCNN–SVM model RNA-Seq gene expression data classification. This model extracts features from RNA-Seq gene expression data. After tuning hyper parameters, the proposed model performs quite comparatively well on the task tested on RNA-Seq gene expression datasets from The Cancer Genome Atlas (TCGA). 25 small and medium RNA-Seq datasets and a large RNA-Seq dataset of 36 cancer type are also added to the experiment.View all notes

ABSTRACT In recent years, cancer is one of the leading causes of death worldwide. Therefore, there are more and more studies that have been conducted to find effective solutions to diagnose and treat cancer. However, there are still many challenges in cancer treatment because possible causes of cancer are genetic disorders or epigenetic alterations in the cells. RNA sequencing is a powerful technique for gene expression profiling in model organisms and it is able to produce information for diagnosing cancer at the biomolecular level. Gene expression data are used to build a classification model which supports treatment of cancer. Nevertheless, its characteristic is very-high-dimensional data which lead to over-fitting issue of classifying model. In this paper, we propose a new gene expression classification model of support vector machines (SVM) using features extracted by deep convolutional neural network (DCNN). In our approach, the DCNN extracts latent features from gene expression data, then they are used in conjunction with SVM that efficiently classify RNA-Seq gene expression data. Numerical test results on RNA-Seq gene expression datasets from The Cancer Genome Atlas (TCGA) illustrate that our proposed algorithm is more accurate than state-of-the-art classifying models including DCNN, SVM and random forests