Computational Research Center for Complex Diseases

Computational
Research Center
for Complex Diseases

From Data to Disease Mechanism and Treatments

About the research center

Each OMF funded study is selected, with oversight from our Scientific Advisory Board, for its ability to provide meaningful data that will fuel the search for treatment and diagnosis. This data is essential to develop diagnostic technologies, understand the molecular basis of the diseases, and uncover effective diagnostic tools and treatments. Under the direction of Wenzhong Xiao, PhD, the Computational Research Center for Complex Diseases analyzes and integrates the data from OMF funded studies, an essential step in developing diagnostics and treatments.

Dr. Xiao is a world expert in computational genomics and the Director of the Immuno-Metabolic Computational Center at Massachusetts General Hospital (MGH), Harvard Medical School. He also leads a Computational Genomics Group at Stanford Genome Technology Center (SGTC). His research is at the interface of computation, genomics and medicine. In collaboration with Open Medicine Foundation, his lab analyzes studies on ME / CFS and compares ME / CFS with other diseases.

Active/Ongoing Studies

Personalized Automated Symptom Summary (PASS)

Researchers are developing a tool to help clinicians more efficiently define the character and priorities of a patient’s current symptoms of ME/CFS, Post-Treatment Lyme Disease, or Fibromyalgia.

Severely ill Patient

The goal of the Severely ill Patient Study was to conduct a comprehensive “Big Data” analysis on severely ill ME/CFS patients in order to begin an exploration to find the molecular basis of ME/CFS.

Autoimmunity and Autoantibodies

The aim is to investigate potential differences in adrenergic and muscarinic receptor autoantibody levels in plasma and cerebrospinal fluid samples between ME/CFS patients and healthy controls.

Deep Assessment of Neuroinflammation Study

We will explore the hypothesis that deranged flow of the cerebrospinal fluid (CSF) due to craniocervical obstructions and/or instability may cause deranged intracranial pressure (ICP), neuroinflammation and cardinal symptoms of ME/CFS.

Deep Proteome and Metabolome Profiling

Decode the molecular mechanisms underlying ME/CFS and contributing to specific symptoms with a particular emphasis of post-exertional malaise (PEM). This includes deep phenotyping of ME patients and global proteomic/metabolomics plasma profiling of ME..

Stress-Activated MicroRNAs

Studying microRNAs could help to bridge the conceptual gap between genetic predisposition and environmental factors causing ME/CFS or exacerbating specific symptoms.

Skeletal Muscle Dysfunction

This project aims to explore the biological changes that occur in the muscles during Post-exertional Malaise (PEM).

Data Sharing in the OMF ME/CFS Data Center

The purpose of the OMF Data Center is to house raw data and processed results, which is shared with our research network through the web-based data portal.

iCPET Omics Studies of ME/CFS

This Harvard study evaluates the different explanations of heart preload failure in ME/CFS patients to determine which is active in many, if not all, ME/CFS patients. The Computation Center is now seeking to better understand the causes of ME/CFS (PLF, the high flow type) as well as to identify potential drug targets for future therapies.

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.

The Life Improvement Trial

The Life Improvement Trial (LIFT) aims to investigate two drugs, separately and in combination, Pyridostigmine (commonly known as Mestinon) and Low-Dose Naltrexone (LDN), for efficacy and to research the difference between responders and non-responders.

Multi-Omic Approaches to Solve Post-Acute COVID-19 (MOSAIC)

The purpose of this study is to facilitate early detection of ME/CFS in people with Long COVID and better understand disease progression.

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