Application of Deep Learning in Microbiome
- 10.2991/jaims.d.201028.001How to use a DOI?
- Microbiome; Deep learning; Phylogeny
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.
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Qiang Zhu AU - Ban Huo AU - Han Sun AU - Bojing Li AU - Xingpeng Jiang PY - 2020 DA - 2020/11/04 TI - Application of Deep Learning in Microbiome JO - Journal of Artificial Intelligence for Medical Sciences SP - 23 EP - 29 VL - 1 IS - 1-2 SN - 2666-1470 UR - https://doi.org/10.2991/jaims.d.201028.001 DO - 10.2991/jaims.d.201028.001 ID - Zhu2020 ER -