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Network Medicine for
Disease Mechanisms and Treatment

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
  • Peng Li, PhD
  • Gonghua Li, PhD
  • Feifei Han, PhD
TECHNOLOGY 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

Knowledge-Based Network Medicine

  • An ME/CFS-specific knowledge base has been established and the computational pipeline has been developed.
  • Diseases similar to ME/CFS and key genes have been identified.
  • Gene targets and initial candidates for treatments have been identified.
  • Two manuscripts are in preparation.

 

Systems Biology Modeling of Metabolic Dysfunctions

  • We focus on uncovering the molecular mechanism of metabolic dysfunctions in patients and identifying key molecular regulators as potential targets for further development of intervention. In addition, we aim to repurpose drugs and nutritional support that can be tested in different groups of patients, i.e. precision medicine for ME/CFS.
  • We have applied the method to predict drugs and supplements to alleviate metabolic dysfunctions seen in the muscles of ME/CFS and Long COVID patients. The computational results will need to be validated.
  • One manuscript is under review and one preprint has been released.

Other links you might be interested in:

OMF Milestones
Melbourne ME/CFS Collaboration
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(EIN# 26-4712664). All donations are tax-deductible to the extent allowed by law.

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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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