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.
Highlights
Deep learning is a recent and fast-growing field of machine learning
The early framework for deep learning was built on artificial neural networks (ANNs) in the 1980s [2], while the real impact of deep learning became apparent in 2006 [3,4]
AdaGrad updates parameters according to the accumulation of squared gradients, which can converge rapidly when applied to convex functions, but performs worse in certain models [62]
Summary
Deep learning is a recent and fast-growing field of machine learning It attempts to model abstraction from large-scale data by employing multi-layered deep neural networks (DNNs), making sense of data such as images, sounds, and texts [1]. The past decades have witnessed a massive growth in biomedical data, such as genomic sequences, protein structures, and medical images, due to the advances of highthroughput technologies. This deluge of biomedical big data necessitates effective and efficient computational tools to store, analyze, and interpret such data [5,8]. We hope this paper will provide readers an overview of deep learning, and how it can be used for analyzing biomedical data
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