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Related Experiment Video

Updated: Aug 14, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Tracing human life trajectory using gut microbial communities by context-aware deep learning.

Haohong Zhang1, Hui Chong1, Qingyang Yu1

  • 1Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.

Briefings in Bioinformatics
|January 11, 2023
PubMed
Summary
This summary is machine-generated.

A new computational method, microDELTA, accurately tracks changes in human gut microbial communities over time. This approach overcomes data limitations and reveals how factors like delivery mode and diet impact microbial composition across the lifespan.

Keywords:
gut microbial communitiesknowledge discoverylife trajectorytransfer learning

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Area of Science:

  • Microbiology
  • Computational Biology
  • Human Health

Background:

  • Human gut microbial communities exhibit dynamic changes throughout life.
  • Understanding the temporal dynamics and influencing factors of gut microbiota is crucial but challenging due to data limitations and batch effects.

Purpose of the Study:

  • To develop a novel computational approach, microDELTA (microbial-based deep life trajectory), for tracking longitudinal alterations in human gut microbial communities.
  • To address context-awareness and data challenges in analyzing microbial community dynamics across different life stages.

Main Methods:

  • Utilized transfer learning for context-aware mining of gut microbial community dynamics.
  • Applied microDELTA to diverse cohorts including infants, travelers, hunter-gatherers, and the elderly.
  • Compared microDELTA's performance against a Neural Network for age prediction in infants.

Main Results:

  • microDELTA accurately predicted infant age, outperforming a Neural Network, particularly for newborns (AUROC 0.811 vs. 0.436).
  • Identified the influence of delivery mode on infant gut microbial communities.
  • Revealed associations between long-term dietary shifts and adult gut microbiota, seasonal microbial cycling in hunter-gatherers, and distinct microbial patterns in the elderly.

Conclusions:

  • microDELTA effectively addresses challenges in tracing the life trajectory of human microbial communities.
  • The developed method generates accurate and flexible models for longitudinal microbial research across various populations and life stages.