Data Driven QSP Modeling of Cancer: A Step Toward Personalized Treatment
October 24, 2025 | 12:00 PM – 1:00 PM
Virtual
Are you interested in how machine learning could open the door to precision medicine?
Join this presentation about a computational framework that blends machine learning and a quantitative systems pharmacology (QSP) model to predict treatment response.
During the webinar, Dr. Leili Shahriyari will introduce the data-driven QSP software that she and her colleagues are developing with funding from NCI’s Informatics and Technology Research program.
She will cover how the model:
- builds on the QSP computational method to analyze drug interactions and effects within tumors.
- incorporates individual patient data in the model to better represent a person’s unique tumor biology.
- simulates the unique characteristics of each tumor and its treatment response.
By using a framework that combines patient data with insights into how cells and molecules interact, this model could better predict how cancer will grow and respond to treatment options.
About the Speaker
Leili Shahriyari, Ph.D.
Dr. Shahriyari is an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst and is affiliated with the UMass Cancer Center. Her team focuses on developing computational models and frameworks to better understand tumorigenesis and optimize cancer treatments, ultimately aiming to develop personalized therapies.