Overcoming regional limitations: transfer learning for cross-regional microbial-based diagnosis of diseases

With great interest, we have read the article by Clooney et al, which highlighted the regional effects on the heterogeneity of the gut microbiota among populations with inflammatory bowel disease (IBD).1 As a result, regional effects would largely limit the microbial-based diagnosis of diseases across regions. Although current machine learning methods based on microbial features have been applied to diagnosis of diseases such as IBD2 and type 2 diabetes,3 these methods are unable to mitigate the regional effects and meet the demand of microbial-based cross-regional diagnosis of diseases. Here, we proposed a machine learning framework (, accessible at: https://github.com/HUST-NingKang-Lab/EXPERT-Disease-GGMP), which integrated the neural network and transfer learning, to effectively reduce regional effects for microbial-based cross-regional diagnosis. Importantly, transfer learning can ‘borrow’ the mature knowledge about diseases from a source city to assist the disease diagnosis for a target city, especially when...
Source: Gut - Category: Gastroenterology Authors: Tags: Open access, Gut PostScript Source Type: research