Network Medicine for
Disease Mechanisms and Treatment

Knowledge-Based Network Medicine

• Establish clinical knowledge base for ME/CFS and related diseases.
  • Developing a clinical knowledge graph system to integrate disease and omics data
  • Curating omics data including genetics, gene expression, proteomics and metabolomics profiles
  • Collecting the co-occurrence events, such as diagnosis, lab tests, meds and procedures
  • Collecting disease-symptom relations for ME/CFS related symptoms, e.g., PEM and OI.
• Build a computational pipeline to train knowledge-based models for prediction of disease gene modules on a high-performance computing cluster.
• Use knowledge-based models to represent candidate drugs for ME/CFS and related diseases for modeling of their mechanisms. 

Systems Biology Modeling of Metabolic Dysfunctions

We developed systems-level metabolic models for viral infected human tissues to understand viral induced metabolic dysfunctions and to identify potential novel treatments.

In collaboration with clinicians at MGH, we identified candidates of autoimmunity for further investigation.