Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.

Background: Technological advances have revolutionised the microbiome research in the field of human reproduction, identifying the microbiome as important regulator of reproductive health and functions, where microbes are now linked to sperm quality, ovarian function, endometrial receptivity, embryo implantation, and pregnancy outcomes, including miscarriage and preterm birth. However, the field faces a 'descriptive phase' due to the fragmentation of datasets and a lack of functional integration. To progress towards broader understanding and clinical utility, robust computational frameworks are required to translate complex microbial signatures into predictive insights. Methods: This review classifies the application of machine learning (ML) and deep learning (DL) into essential methodological pillars. We evaluate the entire computational workflow from sequencing data generation to predictive modelling, including data pre-processing for sparsity and compositionality, exploratory, diversity, functional and differential abundance analyses, and advanced data integration to harmonise data across samples, independent datasets, and multiple 16S regions. Particular emphasis is placed on feature selection for biomarker signatures, synthetic data generation, and phenotype classification. Where reproductive-specific evidence is currently limited, normally due to scarce study cohorts, we present proof-of-concept studies from other human niches to demonstrate potential applications. Furthermore, we address the need for Explainable Artificial Intelligence (XAI) to ensure biological interpretability and guide clinical decisions effectively. Conclusion: Transitioning from descriptive to predictive reproductive medicine relies on integrating ML/DL approaches with biological knowledge. Challenges like small cohort sizes can be overcome through the integration and harmonisation of independent datasets. On a methodological level, researchers should adopt ensemble-based differential abundance and feature selection to reduce data and tool-specific biases. Crucially, any steps altering data nature-such as synthetic data generation-must be strictly confined to training sets to prevent data leakage and preserve model validity. Alongside these technical precautions, standardising analytical workflows and prioritising interpretability are key practical steps. While clinical translation requires extensive validation, moving toward predictive studies is a fundamental first step for future personalised reproductive care. With the current review, we highlight the methodological considerations of ML/DL and provide recommendations for future microbiome studies in the field.