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Volume 10, Issue 4

Deep learning methods in metagenomics: a review Open Access

Abstract

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome’s key role in our health.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): binning , deep learning , disease prediction , embedding , metagenomics , microbiome and neural network
Funding
This study was supported by the:
  • Agence Nationale de la Recherche (Award ANR-21-CE45-0030)
    • Principle Award Recipient: NotApplicable
© 2024 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2024-05-01
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Deep learning methods in metagenomics: a review
M Gen 10, 001231 (2024); https://doi.org/10.1099/mgen.0.001231
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