MSc Thesis Proposal Annoucement of Ann Reba Thomas Alexander:"A Network-Based Approach for Computational Drug Repurposing on Cancer datasets "

 

Date: Tuesday February 16, 2021 

Time: 1:30pm -3:00pm  

Passcode: If interested in attending the event, contact the Graduate Secretary for the passcode at csgradinfo@uwindsor.ca

Breast Cancer is the leading cause of cancer-related death which makes up a 25percent of all new cancer diagnoses globally to the American Cancer Society (ACS). Developing an effective drug can be a time-consuming and expensive crucible. Drug Repurposing is an effective method that takes away both time and cost compared to traditional drug discovery. It is the process of determining whether a drug currently approved for a disease, say disease A, can be indicated for another disease, say disease B. Network-based machine learning methods are used for predicting a given drug for A that can be used for B. It aims at finding new indications for already existing drugs and therefore increases the available therapeutic choices at a fraction of the cost of new drug development. In previous studies, the network-based method is a tremendous platform for drug repositioning as there exist more biological networks that can be used to model the interaction between the biological concepts. In this thesis, we are interested in finding the best drugs that can be repurposed for the disease, Breast Cancer using the existing Protein-protein interaction (PPI) networks. For each gene in the drug dataset, the p-value based on the z-score was calculated. 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. Similarly, Perturbation gene expression profile correlates with the disease gene expression profile, the drug may have an adverse effect on the disease. Six samples of stroma surrounding invasive breast primary tumors and six matched samples of the normal stroma are extracted from the public functional genomics data repository, Gene Expression Omnibus. The perturbation gene expression data corresponding to the MCF7 cell line was extracted from the National Institute of Health’s (NIH), Library of Integrated Network-Based Cellular Signatures (LINCS) dataset. Using Louvain Clustering Algorithm, we detect community Networks and obtain Disease-Drug Data Network. Finally, we propose Edmond's matching algorithm to obtain the best-suited drug that could be repurposed for breast cancer disease. 

 

Keywords : Drug Repurposing ,Protein-protein interaction (PPI) networks, perturbation gene expression, Network-based 

 

Internal Reader: Dr. Dima Alhadidi            

External Reader: Dr. Myron Hlynka           

Advisor: Dr. Alioune Ngom