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  • Review Article
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Ensemble deep learning in bioinformatics

Nature Machine Intelligence volume 2, pages 500–508 (2020)Cite this article

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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.

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Fig. 1: The focus of this Review Article and classic ensemble methods.
Fig. 2: Typical ensemble deep learning frameworks in supervised and unsupervised learning.

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Acknowledgements

P.Y. was supported by an Australian Research Council (ARC) Discovery Early Career Researcher Award (DE170100759) and a National Health and Medical Research Council Investigator Grant (1173469). J.Y.H.Y. and P.Y. were supported by an ARC Discovery Project (DP170100654). Y.C. was supported by a University of Sydney Postgraduate Award. T.A.G. was supported by a postgraduate scholarship from Research Training Program.

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  1. School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia

    Yue Cao, Jean Yee Hwa Yang & Pengyi Yang

  2. Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia

    Yue Cao, Thomas Andrew Geddes, Jean Yee Hwa Yang & Pengyi Yang

  3. School of Environmental and Life Sciences, University of Sydney, Sydney, New South Wales, Australia

    Thomas Andrew Geddes

  4. Computational Systems Biology Group, Children’s Medical Research Institute, University of Sydney, Westmead, New South Wales, Australia

    Pengyi Yang

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P.Y. conceptualized this work. Y.C and P.Y. reviewed the literature and drafted the manuscript. All authors wrote and edited the Review Article.

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Correspondence to Pengyi Yang.

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Cao, Y., Geddes, T.A., Yang, J.Y.H. et al. Ensemble deep learning in bioinformatics. Nat Mach Intell 2, 500–508 (2020). https://doi.org/10.1038/s42256-020-0217-y

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