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Updated: Jun 26, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

The Programmable Microbiome: Integrative AI and Multi-Omics Frameworks for Precision T2DM Management.

Barlina Konwar¹, Kwang-Sun Kim¹

¹Department of Chemistry and Chemistry Institute of Functional Materials, Pusan National University, Busan 46241, Republic of Korea.

|June 25, 2026

Summary

This summary is machine-generated.

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The gut microbiota acts as a programmable organ influencing Type 2 Diabetes Mellitus (T2DM). New research views T2DM as a host-microbe metabolic imbalance, paving the way for precision microbiome engineering.

Area of Science:

Microbiology
Metabolic Engineering
Computational Biology

Background:

The gut microbiota is increasingly recognized as a key regulator of systemic homeostasis.
Recent research (2023-2025) reframes Type 2 Diabetes Mellitus (T2DM) from a host-centric view to one involving host-microbe metabolic dysregulation.

Purpose of the Study:

To review the molecular mechanisms driving the shift in T2DM research toward host-microbe interactions.
To explore the potential of AI and metabolic engineering for precision microbiome interventions.

Main Methods:

Evaluation of microbial metabolite signaling pathways.
Analysis of virome-mediated modulation of host metabolism.
Integration of AI-guided mapping and digital twin simulations for metabolic flux modeling.

Keywords:

Type 2 Diabetes Mellitus artificial intelligence digital twin microbiome engineering pharmaco-microbiomics precision medicine

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Last Updated: Jun 26, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Application of meta-transcriptomics and epigenomics to assess microbiome plasticity.

Main Results:

Microbial metabolites, the virome, and drug-microbiome interactions are critical therapeutic variables in T2DM.
AI and digital twins enable high-resolution metabolic flux modeling and prediction of engineered microbial consortia stability.
Meta-transcriptomics and epigenomics reveal microbiome functional adaptability under therapeutic pressure.

Conclusions:

The gut microbiota can be viewed as a programmable infrastructure for adaptive, real-time interventions.
Precision microbiome engineering, supported by AI analytics, offers a pathway to sustained metabolic resilience in T2DM.
This review provides a blueprint for moving beyond correlative studies to targeted microbiome-based therapies.