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

Area of Science:

• Reproductive medicine
• Microbiome research
• Computational biology

Background:

• Technological advances have highlighted the microbiome's role in human reproduction, influencing fertility and pregnancy outcomes.
• Current research is descriptive due to fragmented data and lack of functional integration, hindering clinical utility.
• Robust computational frameworks are needed to translate microbial signatures into predictive insights for reproductive health.

Purpose of the Study:

• To review and classify machine learning (ML) and deep learning (DL) applications in reproductive microbiome research.
• To evaluate computational workflows from sequencing to predictive modeling, including data pre-processing and integration.
• To emphasize feature selection, synthetic data generation, and phenotype classification for biomarker discovery.

Main Methods:

• Classification of ML/DL methodologies within computational workflows.
• Evaluation of data pre-processing, exploratory, functional, and differential abundance analyses.
• Focus on data integration, feature selection, synthetic data generation, and phenotype classification.

Main Results:

• ML/DL approaches can overcome challenges like small cohort sizes through data integration and harmonization.
• Ensemble methods for differential abundance and feature selection can reduce biases.
• Explainable AI (XAI) is crucial for biological interpretability and clinical decision-making.

Conclusions:

• Integrating ML/DL with biological knowledge is essential for transitioning to predictive reproductive medicine.
• Standardizing analytical workflows and prioritizing interpretability are key practical steps.
• Moving towards predictive studies using ML/DL is fundamental for personalized reproductive care.