# Transfer learning identifies bacterial signatures for cross‐regional diagnosis of type 2 diabetes and enable stage‐sensitive dietary fiber intervention

## TL;DR

A deep learning framework called DeepMicroFinder helps diagnose type 2 diabetes across different regions and identifies how dietary fiber can help.

## Contribution

DeepMicroFinder introduces a transfer learning approach to identify and validate bacterial signatures for T2D diagnosis and dietary intervention.

## Key findings

- DeepMicroFinder identifies microbial markers for T2D using transfer learning.
- The framework enables accurate cross-regional diagnosis of T2D.
- Microbial markers were validated in independent cohorts undergoing dietary fiber interventions.

## Abstract

DeepMicroFinder is a deep learning framework designed to update the existing disease diagnosis model to generate a transfer model by leveraging region‐specific microbiome datasets and transfer learning approach. This framework effectively overcomes the limitation of regional effects in the gut microbiome, enabling accurate cross‐regional disease detection. Microbial markers related to type 2 diabetes (T2D) were identified by DeepMicroFinder, and subsequently validated in independent T2D cohorts undergoing dietary fiber interventions.