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Related Experiment Video

Updated: Feb 21, 2026

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)
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Artificial Intelligence (AI) in microbiome-directed biotherapeutics development.

Debarshi Roy1, Soumita Banerjee2, Alisha Ansari1

  • 1Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, India.

Progress in Molecular Biology and Translational Science
|February 19, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can analyze complex human microbiome data to identify microbial signatures for personalized diagnostics and live biotherapeutic products. This approach aims to restore gut microbial balance and treat diseases linked to dysbiosis.

Keywords:
Artificial IntelligenceBiotherapeuticsDysbiosisHuman MicrobiomeLive Biotherapeutic Products (LBPs)Machine LearningMulti-OmicsPersonalized Medicine

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Area of Science:

  • Microbiome research
  • Computational biology
  • Medical science

Background:

  • The human gut microbiome is vital for health, but imbalances (dysbiosis) are linked to diseases.
  • Live biotherapeutics (LBPs) offer a promising avenue for restoring microbial balance.
  • Individual microbiome variability poses challenges for discovering effective microbial medicines.

Purpose of the Study:

  • To explore how artificial intelligence (AI) can identify patterns in microbiome data.
  • To discuss AI's role in microbiome-derived diagnostics and therapeutics.
  • To highlight AI's potential in discovering novel bacteria for LBPs.

Main Methods:

  • Utilizing advanced AI tools like Machine Learning (ML) and Deep Learning (DL).
  • Analyzing massive amounts of microbiome and host 'omics' data.
  • Identifying microbial "signatures" and complex host-microbe interactions.

Main Results:

  • AI can facilitate the complex analysis required for microbiome research.
  • AI models can identify patterns indicative of disease or health states.
  • AI aids in discovering potential candidates for LBPs by understanding microbial functions.

Conclusions:

  • AI offers significant breakthroughs in analyzing personalized microbiome data.
  • AI can accelerate the development of microbiome-derived diagnostics and therapeutics.
  • Overcoming data challenges is crucial for realizing the full potential of AI in microbiome medicine.