Deep Learning and Its Applications in Biomedicine

Overview

Journal Genomics Proteomics Bioinformatics

Specialty Biology

Date 2018 Mar 10

PMID 29522900

Citations 135

Authors

Chensi Cao

Feng Liu

Hai Tan

Deshou Song

Wenjie Shu

Weizhong Li

Yiming Zhou

Xiaochen Bo

Zhi Xie

Affiliations

Soon will be listed here.

Abstract

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.

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