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Deep learning in bioinformatics: Introduction, application, and perspective in the big data era - 2019

Deep Learning In Bioinformatics: Introduction, Application, And Perspective In The Big Data Era

Research Area:  Machine Learning

Abstract:

Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions.

Keywords:  

Author(s) Name:  Yu Li, Chao Huang, Lizhong Ding, Yijie Pan, Xin Gao

Journal name:  Methods

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.ymeth.2019.04.008

Volume Information:  Volume 166, 15 August 2019, Pages 4-21