The Charles Bronfman Institute for Personalized Medicine

BioMe Partnerships: Where You Come In

BioMe is a resource for sponsored biomedical and genetic research, and The Institute for Personalized Medicine is attracting interest from biotechnology companies and clinical and academic centers of excellence around the world. Our expansive patient pool will help researchers uncover information that could be useful in the pursuit of personalized medicine.

We anticipate that our innovative research approaches will generate breakthroughs in our understanding of how genetic variation and environmental exposure interact to influence personal health or response to treatment.

What You Need to Execute Your Ideas

We offer the following:

  • High quality deoxyribonucleic acid aliquots that may be used for interrogating genetic variation (genotyping) and/or chromosomal abnormalities
  • Plasma
  • Phenotypic data (clinical data and self-reported health history)
  • Genotypic data
  • Re-contacting services (recruitment method for separate, prospective research requiring cohort of interest)
  • Sequencing data

Because phenotype information for BioMe participants is derived by algorithms from comprehensive electronic medical records, BioMe phenotype data is highly versatile. Together with genome-wide genotype data, this information supports numerous internal and external research studies of genotype-phenotype associations. BioServe is the portal through which you can request BioMe’s amenities and is the first step toward having your research proposal effectively reviewed by IPM administration and faculty for scientific merit, clinical relevance, feasibility, and conformity with BioMe Biobank Program policies and guidelines. 

A number of innovative exploratory research studies are underway exploiting the extraordinary richness of the clinical information in these electronic medical records. These research studies apply novel techniques to address:

  • Pleiotropy (when one gene affects more than one phenotype)
  • Multi-scale biology (integrating multiple data sources in advanced computational models)
  • PheWAS (phenome-wide association studies)
  • Topic modeling (a new agnostic machine learning approach to mine abstract complex medical data)

For more information, contact

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