Probiotics in disease prevention and treatment: integration of bioinformatics and machine learning tools for their characterization
TL;DR
This review synthesizes current evidence on probiotics, their health effects, and the scientific principles underlying their viability and functionality under different processing and storage conditions to contribute to the development of more effective, targeted, and scientifically optimized probiotic interventions.
Abstract
Probiotics play an important role in human health, food safety, and industrial applications due to their ability to modulate the gut microbiota and support host physiological functions. Their therapeutic potential has expanded beyond gastrointestinal health to include benefits in metabolic, inflammatory, and infectious diseases. This review synthesizes current evidence on probiotics, their health effects, and the scientific principles underlying their viability and functionality under different processing and storage conditions. Despite their wide-ranging applications, conventional probiotic research and development face important limitations related to experimental speed, predictive accuracy, and the analysis of large and complex biological datasets. These challenges necessitate the integration of advanced artificial intelligence (AI) and machine learning (ML) approaches to accelerate discovery and optimize probiotic performance. Particular focus is placed on applying ML and bioinformatics tools to improve probiotic strain selection, predict survival under environmental stresses, and analyze genomic features associated with probiotic efficacy. Genera such as Lactobacillus and Bifidobacterium have demonstrated clinical benefits in conditions including diarrhea, gastroenteritis, inflammatory bowel diseases, allergic disorders, and metabolic dysfunction, through mechanisms including pathogen inhibition, immune regulation, and enhancement of gut barrier integrity. AI- and ML-based models enable rapid, accurate, and scalable analysis of multidimensional probiotic datasets, overcoming traditional trial-and-error limitations. ML enables accurate prediction of probiotic stability and performance across variable pH, temperature, and formulation environments, while bioinformatics provides deeper insight into strain-level genomic traits and functional pathways. Together, these computational advances contribute to the development of more effective, targeted, and scientifically optimized probiotic interventions.