@article{TEXTUAL,
      recid = {14641},
      author = {Santangelo, Brook E. and Bada, Michael and Hunter,  Lawrence E. and Lozupone, Catherine},
      title = {Hypothesizing mechanistic links between microbes and  disease using knowledge graphs},
      journal = {Scientific Reports},
      address = {2025-02-26},
      number = {TEXTUAL},
      abstract = {Knowledge graphs have been a useful tool for many  biomedical applications because of their effective  representation of biological concepts. Plentiful evidence  exists linking the gut microbiome to disease in a  correlative context, but uncovering the mechanistic  explanation for those associations remains a challenge.  Here we demonstrate the potential of knowledge graphs to  hypothesize plausible mechanistic accounts of host-microbe  interactions in disease. We have constructed a knowledge  graph of linked microbes, genes and metabolites called  MGMLink, and, using a shortest path or template-based  search through the graph and a novel path-prioritization  methodology based on the structure of the knowledge graph,  we show that this knowledge supports inference of  mechanistic hypotheses that explain observed relationships  between microbes and disease phenotypes. We discuss  specific applications of this methodology in inflammatory  bowel disease and Parkinson’s disease. This approach  enables mechanistic hypotheses surrounding the complex  interactions between gut microbes and disease to be  generated in a scalable and comprehensive manner.},
      url = {http://knowledge.uchicago.edu/record/14641},
}