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Understanding gut microbiome-based machine learning platforms: A review on therapeutic approaches using deep
Shilpa Malakar1, Priya Sutaoney1, Harishkumar Madhyastha2
1Department of Microbiology, Kalinga University, Raipur, Chhattisgarh, India.
Chemical Biology & Drug Design
|March 16, 2024
Summary
The human gut microbiome plays a vital role in health, but alterations can lead to infections. Advanced
Area of Science:
- Microbiology and Genomics
- Human Microbiome Research
Background:
- Trillions of symbiotic microbial cells reside in humans, aiding digestion and gut barrier function.
- Imbalances in gut microbiota composition can lead to gastrointestinal infections and dysbiosis-associated diseases.
Purpose of the Study:
- To investigate the composition and function of the human gastrointestinal tract microbiota.
- To explore advanced methods for characterizing microbial communities and their therapeutic potential.
Main Methods:
- Utilized culture-independent techniques, including high-throughput 16S ribosomal RNA (rRNA) sequencing.
- Employed whole-genome shotgun metagenomic sequencing for comprehensive microbiota conformation and diversity analysis.
- Integrated artificial intelligence and deep learning with omics-based methods and microfluidic evaluation.
Main Results:
- Characterized microbial communities in the human gastrointestinal tract.
- Enabled functional studies of the human microbiota through genome mapping and metabolic analysis.
- Enhanced the identification capabilities for thousands of microbes.
Conclusions:
- Metagenomics, integrated with AI and microfluidics, significantly advances gut microbiome research.
- These integrated approaches are crucial for understanding dysbiosis and developing targeted therapies.
