Insights into Microbiomes through the Lens of Omics and Artificial Intelligence - Vol II

This Research Topic is the second volume of Research Topic "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 welcomes original research articles, reviews, methodological papers, benchmarking studies, and case studies at the intersection of microbiome science, omics technologies, and artificial intelligence. It aims to foster interdisciplinary dialogue and showcase novel computational methods for extracting meaningful biological insights from high-dimensional data generated from human, animal, plant, and environmental microbiomes. Submissions may use genomic, transcriptomic, proteomic, metabolomic, or other omics data to address challenges in data integration, feature selection, microbiome classification, functional annotation, network inference, biomarker discovery, predictive modelling, visualization, microbiome-based diagnostics, and clinical or ecological prediction. We particularly encourage studies that apply or develop AI and machine learning approaches, including neural networks, ensemble methods, and explainable AI, to enhance the resolution, accuracy, and interpretability of microbiome analyses. Authors should clearly define the biological problem, describe the computational approach, and explain the biological relevance and interpretability of their findings. Manuscripts must include appropriate computational or experimental validation and be supported by accessible data and code. Contributions that bring together expertise from computational biology, microbiology, systems biology, and data science, and that demonstrate translational or real-world potential, are especially encouraged. All manuscripts will undergo rigorous peer review. Topic editors: Swarna Kanchan, Frederic ANDRES, Dr. Minu Kesheri Source: https://www.frontiersin.org/research-topics/86476/insights-into-microbiomes-through-the-lens-of-omics-and-artificial-intelligence---vol-ii