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Network Medicine to Identify Disease Mechanisms & Treatments

TECHNOLOGY VALUE

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

LEAD INVESTIGATORS
  • Wenzhong Xiao, PhD
  • Jingcheng Yang, PhD
  • Li-Yuan Hung / Chanshuo Wu, PhD
  • Martha Eckey, Pharm D
  • Peng Li, PhD
  • Gonghua Li, PhD
  • Feifei Han, PhD
Study Hypothesis and Description

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.

OBJECTIVES

  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.
  5.  
Updates and Potential
  • We identified diseases similar to ME/CFS and key genes involved.
  • We identified target genes for modulation and are investigating evidence in preclinical animal models and real-world evidence from electronic health records and patient self-reported outcomes.
  • We completed a patient treatment survey of ~4,000 ME/CFS and Long COVID patients on their self-reported outcomes of 150+ treatments. A manuscript has been published: Eckey, M., et al. (2025). Patient-reported treatment outcomes in ME/CFS and long COVID. PNAS. 122(28): e2426874122. doi:10.1073/pnas.2426874122
  • We identified candidate treatments and are discussing with potential partners and collaborators on follow-up human trials and preclinical studies.
  • We continue to retrain the model as new data emerges and identify changes to the results of the model based on the new information.

Consent management

Consent Preferences

Data Subject Access Request (DSAR) Form 

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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