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Network Medicine for
Disease Mechanisms and Treatment

The goal of this project is to use data integration and network analyses to discover disease mechanisms and potential treatments.

  • Wenzhong Xiao, PhD
  • Chia-Jung Chang, PhD
  • Gonghua Li, PhD
  • Joshua Dibble, Ph.D.

Disease knowledge-based networks reflect the current understanding of diseases between clinical phenotypes/symptoms, drugs, genotypes and other molecular interactions.

By applying network of biomedical knowledge to integrate clinical phenotypes and molecular signatures of ME/CFS, we hope to uncover disease gene modules and to prioritize drug candidates for repurposing to help disease symptoms and progression.


  1. Develop a knowledge base of curated studies and the gene-disease-symptom map of ME/CFS available to the research community through the established ME/CFS Data Center.
  2. Conduct a deep-learning heterogeneous networks method for network medicine (MINDR) to discover disease modules and identify drug targets.
  3. Through the computational analysis, identify molecular modules underlying ME/CFS and prioritize in silico drug molecules for repurposing in ME/CFS
  4. Validate the top candidates by evaluation of their reported effects in real world patient data and recommend at least two drugs as candidates for pilot clinical trials.

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.

Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME / CFS) Post Treatment Lyme Disease Syndrome (PTLDS), Fibromyalgia Leading Research. Delivering Hope.Open Medicine Foundation®

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