Background: The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases.
Results: We prepare two benchmark datasets including diabetes and liver cirrhosis to explore the microbe functionality in human diseases. Our model discovered and visualized the novel candidates important microbiome genes and their functions by calculating the important score of each gene and GO term in the network in both datasets. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models to ensure the importance of candidates genes and their functions.
Conclusions: The discovered candidates important microbiome genes and their functions provide novel insights into microbe contribution in functional aspects.