COMPUTATIONAL AND MACHINE LEARNING FRAMEWORKS FOR MICROBIAL DATA ANALYSIS: A SYSTEMATIC REVIEW

Abstract

Machine learning (ML) has emerged as a central computational paradigm for advancing microbial research in genomics, metagenomics, microbiome ecology, medical diagnostics, and industrial biotechnology. The growing scale, complexity, and heterogeneity of microbial datasets generated by high-throughput sequencing, large metagenomic surveys, advanced microscopy, and multi-omics profiling have exceeded the analytical capabilities of traditional statistical and rule-based methods. This review synthesizes current ML methodologies applied to microbial data, with emphasis on the types of microbial datasets that require ML-based analysis—including genomic, metagenomic, imaging, environmental, industrial, and emerging multi-omics data. We examine supervised learning, unsupervised learning, deep learning, and hybrid multi-view approaches, highlighting their applications in taxonomic classification, antimicrobial resistance (AMR) prediction, microbial image interpretation, community structure inference, and functional annotation. Benchmark performance summaries and representative public datasets are provided to contextualize methodological capabilities.

The review also discusses key challenges limiting ML performance in microbial science, including data noise, sparsity, batch effects, incomplete reference databases, limited labelled datasets, computational constraints, and the persistent interpretability gap in complex models. Addressing these challenges is essential for improving generalizability, robustness, and translational applicability. Future research directions identified in this work include multi-omics data integration, development of scalable and efficient ML architectures, incorporation of biological priors into model design, improved benchmarking standards, domain-specific explainable AI (XAI), and responsible governance frameworks for clinical and industrial deployment.

Overall, ML offers transformative potential for understanding microbial diversity, functions, and interactions. As computational techniques become more interpretable, scalable, and biologically informed, ML-driven analysis is poised to play an increasingly pivotal role in environmental microbiology, industrial bioprocessing, and clinical diagnostics.

Keywords: Machine learning, microbial genomics, metagenomics, microbiome analysis, deep learning, supervised learning, unsupervised learning, multi-omics, antimicrobial resistance, microbial imaging, explainable AI, computational biology.