Incorporating metabolic activity, taxonomy and community structure to improve microbiome-based predictive models for host phenotype prediction

ABSTRACT

We developed MicroKPNN, a prior-knowledge guided interpretable neural network for microbiome-based human host phenotype prediction. The prior knowledge used in MicroKPNN includes the metabolic activities of different bacterial species, phylogenetic relationships, and bacterial community structure, all in a shallow neural network. Application of MicroKPNN to seven gut microbiome datasets (involving five different human diseases including inflammatory bowel disease, type 2 diabetes, liver cirrhosis, colorectal cancer, and obesity) shows that incorporation of the prior knowledge helped improve the microbiome-based host phenotype prediction. MicroKPNN outperformed fully connected neural network-based approaches in all seven cases, with the most improvement of accuracy in the prediction of type 2 diabetes. MicroKPNN outperformed a recently developed deep-learning based approach DeepMicro, which selects the best combination of autoencoder and machine learning approach to make predictions, in all of the seven cases. Importantly, we showed that MicroKPNN provides a way for interpretation of the predictive models. Using importance scores estimated for the hidden nodes, MicroKPNN could provide explanations for prior research findings by highlighting the roles of specific microbiome components in phenotype predictions. In addition, it may suggest potential future research directions for studying the impacts of microbiome on host health and diseases. MicroKPNN is publicly available at https://github.com/mgtools/MicroKPNN.

KEYWORDS:

• Gut microbiome
• human disease
• interpretable neural network
• prior-knowledge primed
• metabolic activity
• taxonomy
• bacterial community

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

MicroKPNN is available as an open source repository at https://github.com/mgtools/MicroKPNN.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2024.2302076

Additional information

Funding

This work was supported by NSF under Grant EF-2025451 and NIH under Grant 1R01AI143254.