Classifying gene expression data is known to contain keys for solving the fundamental problems in cancer studies. However, this issue is a complex task because of the large p, small n issue on gene expression data analysis. In this paper, we propose the improvements in the large p, small n classification issue for the study of human cancer. First, a new enhancing sample size method with generative adversarial network is proposed to improve classification algorithms. Second, we suggest a classification approach with over-sampling technique using features extracted by deep convolutional neural network. Numerical test results on fifty very-high-dimensional and low-sample-size gene expression data datasets from the Kent Ridge Biomedical and Array Expression repositories illustrate that the proposed models are more accurate than state-of-the-art classifying models. In addition, we also have explored the performance of support vector machines, k nearest neighbors and random forests, which have improved when apply our approaches.

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.

A: Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. K : Computerized Tomography (CT) and Computed Radiography (CR); Plasma diagnostics -interferometry, spectroscopy and imaging 1Corresponding author. 2See the author list of Overview of the JET preparation for Deuterium-Tritium Operation by E. Joffrin et al. in Nucl.

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