Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data

Abstract

Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq data can provide a novel possibility in the biomedical field. In this study, a novel approach based on a deep learning algorithm and cloud model was developed, named Deep-Cloud. Its main advantage is not only using a convolutional neural network and long short-term memory to extract original data features and estimate gene expression of RNA-seq data but also combining the statistical method of the cloud model to quantify the uncertainty and carry out in-depth analysis of the DEGs between the disease groups and the control groups. Compared with traditional analysis software of DEGs, the Deep-cloud model further improves the sensitivity and accuracy of obtaining DEGs from RNA-seq data. Overall, the proposed new approach Deep-cloud paves a new pathway for mining RNA-seq data in the biomedical field.

Editorial: Machine Learning in Bio-cheminformatics

“…A total of four papers analyzing the complicated biological data are included in this collection. Zhou et al developed a deep neural network-based framework for identifying differentially expressed genes based on RNA sequencing data. Hozumi et al processed the single-cell RNA sequencing data using their correlation clustering and projection (CCP) method.…”