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Related Experiment Video

Updated: Jan 8, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

Machine Learning in Microbiome Research and Engineering.

Ryan De Sotto^1,2,3,4, Nikhil Aggarwal^1,2,3,4, Elizabeth Huiwen Tham^1,2,5,6

¹NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore.

ACS Synthetic Biology

|December 22, 2025

Summary

This summary is machine-generated.

Machine learning (ML) advances microbiome engineering by analyzing complex microbial communities and predicting interactions. This enables targeted manipulation for healthcare and agriculture, overcoming data limitations for future applications.

Keywords:

machine learning microbiome engineering microbiome research synthetic biology

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Published on: May 16, 2022

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Published on: October 15, 2019

Area of Science:

Microbiome research
Computational biology
Synthetic biology

Background:

Microbiomes are crucial for healthcare, agriculture, and bioproduction.
Engineering microbiomes requires understanding complex microbial interactions.
Machine learning (ML) offers powerful tools for analyzing microbiome data.

Purpose of the Study:

To examine the transformative applications of ML in microbiome engineering.
To highlight ML's role in microbiome data analysis and targeted manipulation.
To discuss challenges and future perspectives in ML-driven microbiome engineering.

Main Methods:

Utilizing ML algorithms for analyzing large microbiome datasets.
Predicting microbial interactions and community behavior.
Employing strategies like quorum sensing molecule manipulation, antimicrobial peptides, and bacteriophages for targeted manipulation.

Main Results:

ML has shown success in identifying microbial communities and predicting interactions.
ML aids in optimizing microbial community design for specific functions.
Integration of ML with experimental approaches is advancing microbiome engineering.

Conclusions:

ML is revolutionizing microbiome engineering, enabling novel applications.
Challenges include data limitations, biological complexity, and integrating computational/experimental methods.
Future advancements in data generation, algorithms, and collaboration will unlock microbiome potential.