Enabling technology for microbial source tracking based on transfer learning: From ontology-aware general knowledge to context-aware expert systems

Habitat specific patterns reflected by microbial communities, as well as complex interactions between the community and their environments or hosts9 characteristics, have created obstacles for microbial source tracking: diverse and context-dependent applications are asking for quantification of the contributions of different niches (biomes), which have already overwhelmed existing methods. Moreover, existing source tracking methods could not extend well for source tracking samples from understudied biomes, as well as samples from longitudinal studies. Here, we introduce EXPERT (https://github.com/HUST-NingKang-Lab/EXPERT), an exact and pervasive expert model for source tracking microbial communities based on transfer learning. Built upon the biome ontology information and transfer learning techniques, EXPERT has acquired the context-aware flexibility and could easily expand the supervised model9s search scope to include the context-dependent community samples and understudied biomes. While at the same time, it is superior to current approaches in source tracking accuracy and speed. EXPERT9s superiority has been demonstrated on multiple source tracking tasks, including source tracking samples collected at different disease stages and longitudinal samples. For example, when dealing with 650 samples from a recent study of colorectal cancer, EXPERT could achieve an AUROC of 0.95 when predicting the host9s phenotypical status. In summary, EXPERT has unleashed the potential of model-based source tracking approaches, enabling source tracking in versatile context-dependent settings, accomplishing pervasive and in-depth knowledge discovery from microbiome.