Applications of machine learning in microbial source tracking.

Machine learning significantly enhances the accuracy, resolution, and scalability of microbial source tracking across various applications.

• Why it matters: Understanding microbial sources is crucial for environmental, food safety, and forensic purposes, but current methods face challenges in handling complex microbiome data and ecological dynamics.
• What they did: The review summarizes major machine learning techniques, tools, and applications in pollution tracing, geospatial attribution, food safety, and forensics, emphasizing their improvements over traditional methods.
• The result: Current studies indicate that machine learning substantially improves MST performance, and future advances in deep learning may further develop robust, interpretable, and scalable source tracking solutions.

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