AI Enabled Subnetwork Selection

Cancers are heterogeneous disease that hijack many of the body’s normal biological processes. Additionally, tens of thousands of genes are involved in each person’s normal biology, while only a fraction of those are repurposed by cancers to drive disease. At an individual level, utilizing entire transcriptomes is rare, as there is too much information for clinicians to process. However, not using this resource can mean important genes and processes are missed. Identifying the set of genes that drive a patient’s cancer would improve therapy design, patient quality of life and outcomes. This project utilizes AI-technologies to identify subnetworks of genes that characterize patient cancers.

Faculty Supervisor:

Linglong Kong;Jack Tuszynski

Student:

Mehrnoosh Bazrafkan

Partner:

CSTS Health Care

Discipline:

Statistics / Actuarial sciences

Sector:

Professional, scientific and technical services

University:

University of Alberta