Predicting drug response in cancer (Masters option available)

Predicting drug response in cancer (Masters option available)

Project details

Molecular data can be used to better understand how patients respond to cancer therapies. While genomic data is the most common kind of data used to stratify cancer patients, pharmacogenomic data can also provide useful information relating to drug response outcome in cancer samples. This project will focus on using different molecular data and network analysis approaches to develop methods for predicting a patient’s response to therapy. 

This project is ideal for a student with a computer science and statistics background who wishes to develop their skills in bioinformatics and computational biology, and gain experience working in pharmacogenomics. 

About our research group

The Davis laboratory is a computational biology group which is focused on the analysis and reconstruction of regulatory systems that link genome, transcriptome, proteome and phenotype. Our work is focused on exploring mechanisms of cancer progression and response to therapy, including regulatory programs such as epithelial-mesenchymal transition. We have a general interest in how cancerous cells hijack various regulatory mechanisms that allow unregulated growth or confer the ability to metastasise and form new tumors. While our laboratory has an established breast cancer research program, we have a general interest in different cancer types. 

Researchers:

Dr Melissa Davis

Portrait photo of Dr Melissa Davis
Dr
Melissa
Davis
Laboratory Head

Project Type: