Tumor gene expression data classification via sample expansion-based deep learning

Abstract

Since tumor is seriously harmful to human health, effective diagnosis measures are in urgent need for tumor therapy. Early detection of tumor is particularly important for better treatment of patients. A notable issue is how to effectively discriminate tumor samples from normal ones. Many classification methods, such as Support Vector Machines (SVMs), have been proposed for tumor classification. Recently, deep learning has achieved satisfactory performance in the classification task of many areas. However, the application of deep learning is rare in tumor classification due to insufficient training samples of gene expression data. In this paper, a Sample Expansion method is proposed to address the problem. Inspired by the idea of Denoising Autoencoder (DAE), a large number of samples are obtained by randomly cleaning partially corrupted input many times. The expanded samples can not only maintain the merits of corrupted data in DAE but also deal with the problem of insufficient training samples of gene expression data to a certain extent. Since Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) models show excellent performance in classification task, the applicability of SAE and 1-dimensional CNN (1DCNN) on gene expression data is analyzed. Finally, two deep learning models, Sample Expansion-Based SAE (SESAE) and Sample Expansion-Based 1DCNN (SE1DCNN), are designed to carry out tumor gene expression data classification by using the expanded samples. Experimental studies indicate that SESAE and SE1DCNN are very effective in tumor classification.

FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier

“…FloWPS combines somehow two methods, SVM and kNN (Altman, 1992), where kNN plays a particular role to extract informative features. The idea to combine feature extraction methods with SVM is well known (Tan and Gilbert, 2003; Kourou et al, 2015; Tan, 2016; Liu et al, 2017; Tarek et al, 2017). The approach proposed in this paper, however, is in principle a novelty, at least because its selection capacity is focused on every single point available for prediction.…”

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

“…This problem may lead to deviation and over-fitting, which may bring about inestimable errors in the prediction [31,32]. Although the regularization technique and reducing the dimension of feature can screen valuable information to some extent, the expansion of sample data is still the most effective means to enhance training [33,34]. With the development of remote sensing (RS) and geographic information system (GIS) technologies, high-resolution digital elevation models (DEM) and engineering data are more easily obtained.…”

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

“…Although a majority of the methods applying deep learning in cancer research are for processing images (e.g. mammograms) [16] [17] [18] [19] and gene expression profiles [20][21] [22], more recently, deep-learning based NLP systems are gaining prominence for cancer information extraction from EHRs [23][24] [25] [26] [27]. For example, Gao et al [26] implemented a hierarchical attention network for extracting some of the crucial clinical oncology data elements such as primary cancer site and histological grade which are gathered by cancer registries.…”