Version 1
: Received: 25 July 2023 / Approved: 26 July 2023 / Online: 27 July 2023 (08:16:56 CEST)
How to cite:
Patil, A.; Singh, N. Artificial intelligence for Microbiota Analysis: Advances and Applications. Preprints2023, 2023071765. https://doi.org/10.20944/preprints202307.1765.v1
Patil, A.; Singh, N. Artificial intelligence for Microbiota Analysis: Advances and Applications. Preprints 2023, 2023071765. https://doi.org/10.20944/preprints202307.1765.v1
Patil, A.; Singh, N. Artificial intelligence for Microbiota Analysis: Advances and Applications. Preprints2023, 2023071765. https://doi.org/10.20944/preprints202307.1765.v1
APA Style
Patil, A., & Singh, N. (2023). <strong>Artificial intelligence for Microbiota Analysis: Advances and Applications</strong>. Preprints. https://doi.org/10.20944/preprints202307.1765.v1
Chicago/Turabian Style
Patil, A. and Neha Singh. 2023 "<strong>Artificial intelligence for Microbiota Analysis: Advances and Applications</strong>" Preprints. https://doi.org/10.20944/preprints202307.1765.v1
Abstract
The body's billions of bacteria—the microbiota—influence health and illness. The microbiome—the genetic material of these microbes—affects digestion, immunological function, and mental health. Due to bacterial variety, complicated relationships, and research technique constraints, investigating the microbiota is difficult. AI and machine learning have expanded microbiome research. Artificial intelligence (AI) systems can efficiently scan large microbiome datasets to help researchers understand microbial populations and their functions. AI-based predictive algorithms can analyze food impacts on microbial communities and forecast illness risks based on gut microbiota composition. AI helps find microbial biomarkers linked to specific health disorders, enabling early illness identification and targeted therapy. AI-driven medication development platforms also modulate microbiome to treat microbiota-related diseases. Understanding microbial populations' involvement in health and illness requires understanding complicated microbial interactions within the microbiome and between it and the host. AI systems improve human health by comprehending these complex relationships. AI in microbiota study must overcome various obstacles. Data quality and AI model interpretability are essential for accurate findings. Diverse and representative datasets avoid biases and strengthen AI-driven conclusions. AI in microbiota research requires data protection, informed permission, and appropriate use of sensitive data. AI is improving our understanding of the human microbiome and its health effects, changing microbiota research. Overcoming difficulties and adhering to ethical norms will enable the proper use of AI-driven findings, leading to microbiome insights and precision medical breakthroughs.
Biology and Life Sciences, Immunology and Microbiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
