Deep learning in microbiome analysis: a comprehensive review of neural network models

Area of Science:

• Microbiology
• Computational Biology
• Bioinformatics

Background:

• Microbiome research studies microbial communities in various environments.
• Deep learning (DL) methods are increasingly integrated into microbiome research.
• Microbiome data is complex, high-dimensional, and comprises diverse omics datasets.

Purpose of the Study:

• To provide an overview of deep learning models used in microbiome research.
• To discuss the strengths, applications, and implications of DL in this field.
• To examine how DL solves key microbiome problems and overcomes limitations.

Main Methods:

• Review of existing literature on deep learning applications in microbiome research.
• Analysis of different deep learning models, including their capabilities in pattern recognition and feature extraction.
• Discussion of challenges and potential solutions for implementing DL in microbiome studies.

Main Results:

• Deep learning excels at pattern recognition, feature extraction, and predictive modeling in microbiome data.
• DL automates the identification of functional genes, microbial interactions, and host-microbiome dynamics.
• Current DL approaches face challenges related to biological variability and data complexity.

Conclusions:

• Deep learning offers unprecedented precision in understanding microbiome composition and its impact on health and the environment.
• Overcoming DL challenges is crucial for advancing microbiological insights and enabling practical applications.
• DL has a transformative potential for the future of microbiome research.