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Pathogenicity Classification of Somatic Mutations in Precision Medicine
Start Date: 12/7/2022Start Time: 3:00 PM
End Date: 12/7/2022End Time: 5:00 PM

Event Description
BIOMED PhD Research Proposal

Title:
Pathogenicity Classification of Somatic Mutations in Precision Medicine

Speaker:
Ashkan Bigdeli, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisors:
Ahmet Sacan, PhD
Associate Teaching Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Uri Hershberg, PhD
Associate Professor
University of Haifa
School of Biomedical Engineering, Science and Health Systems
Drexel University

Robert Babak Faryabi, PhD
Assistant Professor  
Perelman School of Medicine
University of Pennsylvania

Details:
The accurate classification of genetic variations identified using next-generation sequencing (NGS) has become integral to diagnostics and prognostics pan-cancer; yet the classification process of these variants remains highly manual, inherently variable, and largely inaccessible outside of specialized laboratories. While variant data consortiums and professional guidelines assist in variant classification there is a critical need for accessible methods that reduce not only the time burden, but also the observed variability in variant classification.

To this end, we have developed a predictive machine learning algorithm that leverages large pan-cancer cohorts and valuable domain resources to effectively grade the pathogenicity of variants derived from DNA NGS. This work increases the accessibility of variant classification through distributable software and decreases variability and time burden through automation. The ability to use accurate predictive models allows for the high throughput genomic profiling of disease that we seek to apply in adult B-cell Acute Lymphoid Leukemia to stratify risk and explore clinical outcomes.
Contact Information:
Name: Natalia Broz
Email: njb33@drexel.edu
Ashkan Bigdeli
Location:
Bossone Research Center, Room 709, located at 32nd and Market Streets. Also on Zoom.
Audience:
  • Undergraduate Students
  • Graduate Students
  • Faculty
  • Staff

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