Application of Deep Learning in Microbiome

Authors

  • Qiang Zhu School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei, China
  • Ban Huo Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China
  • Han Sun School of Mathematics and Statistics, Central China Normal University, Wuhan, Hubei, China
  • Bojing Li Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China
  • Xingpeng Jiang School of Computer, Central China Normal University, Wuhan, Hubei, China https://orcid.org/0000-0002-8848-9300

DOI:

https://doi.org/10.2991/jaims.d.201028.001

Keywords:

Microbiome, Deep learning, Phylogeny

Abstract

With the rapid development of high-throughput sequencing technology, massive microbial data has been accumulated. The understanding of the microbial data could help us to find the relationships between microbes and diseases. However, due to the high dimensionality, sparseness, and complexity of the data, traditional machine learning methods have insufficient learning and representational ability. Meanwhile, the rise of deep learning enables us to deal with these complex problems effectively. In this survey, we introduce the application of machine learning in microbial data analysis and focus on microbial classification and feature selection tasks. In particular, we discuss the current application and challenges of deep learning in microbial studies. Based on these discussions, we recommend that before using deep learning to conduct microbiome-wide association studies, it is essential to consider prior knowledge such as phylogeny, which would improve the accuracy and interpretability of the model.

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Published

2021-05-05

How to Cite

1.
Zhu Q, Huo B, Sun H, Li B, Jiang X. Application of Deep Learning in Microbiome. JAIMS [Internet]. 2021 May 5 [cited 2023 Feb. 4];1(1-2):23-9. Available from: http://oapublishing-jaims.com/jaims/article/view/75