Modeling disease networks using graph machine learning

Using Simmunome simulations, researchers and companies can predict the likelihood of success or failure before embarking on, or continuing with, costly clinical development programs. We focus on understanding the biological system and applying this towards higher accuracy in disease simulations. We achieve this by using different types of data from various public and proprietary sources to better characterize the biological system. This process requires increasing complexity in our AI algorithms which therefore significantly increases the complexity of the computational models. The aim of this partnership is to research and apply cutting edge machine learning approaches from the fast-moving AI domain graph machine learning by working with experts in this area.

Faculty Supervisor:

Gilles Caporossi

Student:

Partner:

Simmunome Inc.

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

HEC Montréal

Program: