Molecular Pathology and Genomics & Computational Pathology Symposium

May 5, 2021 - Virtual

The "omics" revolution has arrived!

Curious about the revolutionary role of mass spectrometry in personalized medicine? Join the Department of Laboratory Medicine & Pathology’s Divisions of Molecular Pathology and Genomics (MPG) and Computational Pathology (CP) to learn about how the integration of genomicsproteomics, and metabolomics is challenging traditional medical paradigms and creating novel opportunities for improved, personalized patient care.

Dr. Stefani Thomas, University of Minnesota
Session 1- 10:30AM to 11:30AM 
Big Data, Health and COVID-19
Dr. Michael Snyder, Stanford University
Session 2- 11:30AM-12:00PM
Computational methods for detection of DNA adducts using mass spectrometry
Dr. Scott Walmsley, University of Minnesota
Session 3- 12:00PM-1:00PM
Highlights of the HUPO Human Proteome Project with an example of proteo-genomics of cancers
Dr. Gil Omenn, University of Michigan
Panel Discussion 1:00PM to 1:30PM
Moderated by Dr. Stefani Thomas, University of Minnesota


2020 MPG/CP Symposium

July 23, 2020

9:00–10:00 - Keynote Speaker 1: Colin Pritchard, MD, PhD 

Associate Professor, Genetics
Associate Director, Genetics and Solid Tumors Laboratory
University of Washington Medical Center

10:00–10:30 - Local Talk 1: Andrew Nelson, MD, PhD

Associate Professor, Laboratory Medicine and Pathology
University of Minnesota

The Ovarian Cancer Precision Medicine Initiative

10:30–11:30 - Keynote Speaker 2: Jochen Lennerz, MD, PhD

Medical Director, Center for Integrated Diagnostics, Massachusetts General Hospital
Associate Professor of Pathology, Harvard Medical School
Assistant Pathologist, Pathology, Massachusetts General Hospital

The Use of Artificial Intelligence in the Clinical Report of Genetic Variants at Massachusetts General Hospital 

11:30–12:00 - Local Talk 2: Kelsey McIntyre, PhD

Assistant Professor, Laboratory Medicine and Pathology
University of Minnesota

Clinical Validation of Whole Genome Sequencing for Detection of Structural Variants