Published on Thu Feb 28 2019

Deep learning in bioinformatics: introduction, application, and perspective in big data era

Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li, Yijie Pan, Xin Gao

Deep learning 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. In this review, we provide both the esoteric introduction of deep learning, and concrete examples and

0
0
0
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. The implementations are freely available at \url{https://github.com/lykaust15/Deep_learning_examples}.

Mon Mar 21 2016
Machine Learning
Deep Learning in Bioinformatics
Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry.
0
0
0
Sat May 29 2021
Machine Learning
Ten Quick Tips for Deep Learning in Biology
Machine learning is a modern approach to problem-solving and task automation. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. The goal was to articulate a practical, accessible, and concise set of guidelines to follow when using deep learning.
12
141
473
Tue Feb 27 2018
Machine Learning
Bioinformatics and Medicine in the Era of Deep Learning
Deep learning has become a disruptive advance in machine learning. It has the potential to become one of the largest data repositories in the world. This article is part of a series of articles on deep learning.
0
0
0
Fri Feb 02 2018
Machine Learning
Deep Learning for Genomics: A Concise Overview
High-throughput sequencing has driven modern genomic studies into "big data" disciplines. Genomics entails unique challenges to deep learning since we are expecting superhuman intelligence. A powerful deep learning model should rely on the utilization of task-specific knowledge.
0
0
0
Thu May 30 2019
Machine Learning
ImJoy: an open-source computational platform for the deep learning era
ImJoy is a browser-based platform designed to facilitate widespread reuse of deep learning solutions in biomedical research. We highlight ImJoy's main features and illustrate its functionalities with deep learning plugins for mobile and interactive image analysis and genomics.
0
0
0
Tue Aug 25 2020
Machine Learning
Towards Structured Prediction in Bioinformatics with Deep Learning
In bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. Structured prediction is more complicated than the traditional classification but has much broader applications. Here, we argue that the following ideas can help resolve structured prediction problems in bioinformics.
0
0
0