The intricate interplay between microbiomes and their hosts profoundly shapes health, disease, and evolution across diverse organisms. Recent advances in computational biology and bioinformatics have empowered researchers to untangle these complex networks, offering novel insights that traditional experimental techniques alone cannot provide.

This Research Topic aims to spotlight state-of-the-art computational and bioinformatic approaches for elucidating microbiome–host interactions. We invite submissions showcasing methodological innovations, integrative analyses, and novel applications that deepen our understanding of these multifaceted relationships.

Areas of interest include, but are not limited to:

o Multi-omics integration: Approaches that leverage metagenomics, metatranscriptomics, metabolomics, and other omics data to model host–microbe interactions at multiple molecular levels.

o Network-based modeling: Techniques to infer microbial community dynamics, host response pathways, and interaction networks.

o Machine learning and AI: Predictive models that unravel microbiome-mediated host phenotypes and outcomes, including applications of deep learning and statistical learning strategies.

o Computational tools: New software, pipelines, or algorithms for characterizing microbiome functional roles in health, disease, and therapeutics.

o Evolutionary and ecological perspectives: Methods that shed light on how microbiomes and their hosts adapt and co-evolve in changing environments.

We welcome original research articles, comprehensive reviews, methods papers, and perspectives that push the boundaries of computational biology in the context of microbiome–host interactions. By assembling a diverse range of contributions, this Topic seeks to foster cross-disciplinary dialogue and accelerate progress in this fascinating field.

Are you interested in contributing a manuscript to this Research Topic?

Computational Approaches to Decipher Microbiome–Host Interaction Networks