Research Area:  Machine Learning

Abstract:

The remarkable flexibility and adaptability of ensemble methods and deep learning models have led to the proliferation of their application in bioinformatics research. Traditionally, these two machine learning techniques have largely been treated as independent methodologies in bioinformatics applications. However, the recent emergence of ensemble deep learning—wherein the two machine learning techniques are combined to achieve synergistic improvements in model accuracy, stability and reproducibility—has prompted a new wave of research and application. Here, we share recent key developments in ensemble deep learning and look at how their contribution has benefited a wide range of bioinformatics research from basic sequence analysis to systems biology. While the application of ensemble deep learning in bioinformatics is diverse and multifaceted, we identify and discuss the common challenges and opportunities in the context of bioinformatics research. We hope this Review Article will bring together the broader community of machine learning researchers, bioinformaticians and biologists to foster future research and development in ensemble deep learning, and inspire novel bioinformatics applications that are unattainable by traditional methods.

Keywords:  

Author(s) Name:  Yue Cao, Thomas Andrew Geddes, Jean Yee Hwa Yang & Pengyi Yang

Publisher name:  Springer Nature

DOI:  10.1038/s42256-020-0217-y

Volume Information:  volume 2, pages: 500–508 (2020)

Paper Link:   https://www.nature.com/articles/s42256-020-0217-y