Machine learning-based data analysis for cancer targeted gene panels

Cancer develops from the accumulation of mutations in key genes, which drive disease progression in individual patients. Currently, cancer genomic analysis results in the reporting of dozens to hundreds of mutations and other genomic alterations, without providing any indication of which genes are the most functional, and relevant for the survival of the cancer cell.
In this project, we aim at performing a comprehensive targeted gene sequencing of DNA and RNA extracted from over 60 cancer patient samples. Our main goal is to develop a bioinformatics pipeline to perform variant calls on the sequencing data, which will be used to generate reports on functional oncogenes and effective targeted drugs in individual cancer patient samples.
Our main methodology will utilize Artificial Intelligence techniques for Deep Machine Learning to understand the contributions, interactions, and relationships among the genomic variants. We will validate our findings by using existing large-scale cancer specific genomic databases.

Faculty Supervisor:

Luis Rueda

Student:

Partner:

ITOS Oncology

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Windsor

Program: