Application of deep learning in genomics
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Jianxiao Liu , Jiying Li , Hai Wang & Jianbing Yan 1
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; 2 College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; 3 Microsoft Corporation, Redmond 98052, USA; 4 National Maize Improvement Center, Key Laboratory of Crop Heterosis and Utilization, Joint Laboratory for International Cooperation in Crop Molecular Breeding, China Agricultural University, Beijing 100193, China Received June 13, 2020; accepted August 15, 2020; published online October 10, 2020
In recent years, deep learning has been widely used in diverse fields of research, such as speech recognition, image classification, autonomous driving and natural language processing. Deep learning has showcased dramatically improved performance in complex classification and regression problems, where the intricate structure in the high-dimensional data is difficult to discover using conventional machine learning algorithms. In biology, applications of deep learning are gaining increasing popularity in predicting the structure and function of genomic elements, such as promoters, enhancers, or gene expression levels. In this review paper, we described the basic concepts in machine learning and artificial neural network, followed by elaboration on the workflow of using convolutional neural network in genomics. Then we provided a concise introduction of deep learning applications in genomics and synthetic biology at the levels of DNA, RNA and protein. Finally, we discussed the current challenges and future perspectives of deep learning in genomics. deep learning, genomics, convolutional neural network Citation:
Liu, J., Li, J., Wang, H., and Yan, J. (2020). Application of deep learning in genomics. Sci China Life Sci 63, https://doi.org/10.1007/s11427-0201804-5
Introduction Artificial intelligence (AI) powers many aspects of modern society, from traditional industries (agriculture, industry, transportation, etc) to modern industries (education, culture, catering, tourism, etc), and it continues to transform more and more sectors. As the core technology in artificial intelligence, machine learning studies the algorithms that computer systems utilize to perform tasks by learning from data instead of following explicit instructions. Despite their extensive applications, conventional machine learning techniques are limited in their capability to process natural data in their raw forms and learn intricate patterns in complex
*Corresponding authors (Jianbing Yan, email: [email protected]; Hai Wang, email: [email protected])
dataset. Compared to conventional machine learning algorithms, deep neural network (DNN) stands out with the ability of automatic feature extraction and greater data representation capability in dealing with high-dimensional datasets. This method has gained dramatically improved performance compared to the state-of-the-art in dealing with the complex classifica
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