Abstract

The integration of AI and deep learning has opened new horizons in microbiome research, bringing innovative solutions for the analysis and interpretation of complex biological data. This paper explores the application of AI-driven deep learning techniques in microbiome data analysis, focusing on their tasks, such as taxonomic profiling, functional annotation, and prediction of host-microbiome interactions. Researchers can process high-dimensional data, identify intricate patterns, and generate actionable insights with unprecedented accuracy by using advanced algorithms like convolutional neural networks and recurrent neural networks. It further discusses the challenges of implementing these technologies, including data heterogeneity, model interpretability, and computational demands, and provides strategies for overcoming these. Emphasizing the transformative potential of AI, this research highlights its capacity to drive breakthroughs in microbiome science, which can allow for health diagnostics, environmental sustainability, and personalized medicine.