Date: Monday October 21, 2019

Time:  3:00pm to 4:30pm

Location: Lambton Tower Room 3105

 

A pharmaceutical drug development is a complex, time-consuming and expensive process which is also limited to a relatively small number of targets. Drug repositioning is a vital function which involves finding new uses and indications for already approved and existing drugs. It is a cost-effective process in contrast to experimental drug discovery. Previous studies have shown that the network-based method is a versatile platform for drug repositioning as there exists more biological networks which can be used to model interaction between the biological concepts. In this thesis, we are interested in finding the best drugs that can be repurposed for the diseases, Breast Cancer and Prostate Cancer using the existing Protein-protein interaction (PPI) networks. The Network data is obtained by mapping the Differentially Expressed Genes in the disease data to the PPI network. The proposed method is based on the idea that if a perturbation gene expression profile inversely correlates with the disease gene expression profile, the drug may have a curing effect on the disease. Six samples of stroma surrounding invasive breast primary tumours and six matched samples of normal stroma are extracted from the public functional genomics data repository, Gene Expression Omnibus. The perturbation gene expression data corresponding to MCF7 cell line was extracted from the National Institute of Health’s (NIH) Library of Integrated Network-Based Cellular Signatures (LINCS) dataset. Finally, Machine Learning techniques are used to select the best suited drug that could be repurposed for the breast and prostate cancer disease.

 

Thesis Committee: 

MSc Thesis Proposal Announcement

5113 Lambton Tower, 401 Sunset Ave., Windsor ON., N9B 3P4 (519) 253-3000 Ext. 3716 csgradinfo@uwindsor.ca