Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges.

Area of Science:

• Microbial Ecology
• Bioinformatics
• Environmental Science

Background:

• Microbial communities possess extensive metabolic functions and respond to environmental changes with specific markers.
• Microbial omics data generates vast, complex datasets rich in information for environmental forecasting.
• Machine learning (ML) and artificial intelligence (AI) are increasingly used to address complex data challenges.

Purpose of the Study:

• To provide an overview of ML applications in microbial ecology.
• To discuss the integration of biological sample information for supervised ML.
• To review the current state and future potential of ML-driven microbial ecology for environmental applications.

Main Methods:

• Review of existing literature on ML and AI in microbial ecology.
• Discussion of workflow considerations for applying ML to microbial data.
• Highlighting the importance of retaining biological sample information for supervised ML models.

Main Results:

• The intersection of molecular microbial ecology and AI shows significant promise for advancing environmental monitoring.
• ML algorithms can uncover hidden patterns within complex microbial datasets.
• Current limitations and future directions for ML-driven microbial ecology are identified.

Conclusions:

• ML-driven microbial ecology is an emerging field with the potential to revolutionize environmental monitoring and management.
• Further research and development are needed to fully realize the capabilities of this interdisciplinary approach.
• This field is expected to become a pivotal toolkit for addressing future environmental challenges.