Insights into Microbiomes through the Lens of Omics and Artificial Intelligence

Microbiomes—complex communities of microorganisms living in diverse environments—play critical roles in human health, agriculture, and ecosystem functioning. The advent of high-throughout omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has revolutionized our ability to profile these microbial communities at unprecedented resolution. However, the volume and complexity of omics data pose significant analytical challenges. Artificial Intelligence (AI), particularly machine learning and deep learning, has emerged as a transformative tool to interpret these datasets, uncovering hidden patterns and predicting functional
interactions within microbiomes. This synergy of omics and AI is unlocking new frontiers in microbiome research, from disease diagnostics to environmental monitoring.

This research topic aims to highlight cutting-edge advancements at the intersection of microbiome science, omics technologies, and artificial intelligence. The primary goal is to foster interdisciplinary dialogue and disseminate novel methodologies and insights
that leverage AI to interpret multi-omics data from microbial communities. We seek contributions that address challenges in data integration, feature selection, predictive modeling, and visualization in microbiome research. By bringing together experts from computational biology, microbiology, systems biology, and data science, this collection will explore how AI enhances the resolution, accuracy, and interpretability of microbiome studies. Topics of interest include, but are not limited to, AI-based tools for microbiome classification, functional annotation, network inference, and clinical or ecological prediction models. We also welcome review articles, benchmarking studies, and case studies demonstrating real-world applications. Ultimately, the goal is to deepen our understanding of microbiome dynamics and functionality by showcasing how intelligent computational frameworks can extract meaningful biological insights from high-dimensional omics datasets.

This research topic welcomes original research articles, reviews, and methodological papers at the interface of omics-based microbiome analysis and artificial intelligence. Submissions may address human, animal, plant, or environmental microbiomes using genomic, transcriptomic, proteomic, or metabolomic data. We especially encourage work that applies or develops AI/ML models—such as neural networks, ensemble methods, or explainable AI—for tasks like biomarker discovery, multi-omics integration, functional inference, and microbiome-based diagnostics. Authors should clearly articulate the biological problem, computational approach, and
interpretability of findings. Studies must include validation strategies, either computational or experimental, and ideally be supported by accessible data and code. Contributions with cross-disciplinary insights and translational potential are highly encouraged. All manuscripts will undergo rigorous peer review.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Microbiomes, Omics, Artificial intelligence, Machine learning, Human Diseases, Plant health, Nutrient Cycling, Genomics, Transcriptomics, Metabolomics, Multi-Omics, Metabolism, Plant-microbes interactions, Wastewater, Freshwater and Marine ecosystems, Microbiology

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.