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MSc Thesis Defense Announcement by Pavithra Ulaganathan:"Network-based Computational Drug Repurposing and Repositioning for Breast Cancer Disease"

Wednesday, January 22, 2020 - 09:30 to 11:00

SCHOOL OF COMPUTER SCIENCE

 

The School of Computer Science is pleased to present...

MSc Thesis Defense by:  Pavithra Ulaganathan

 
Date: Wednesday January 22, 2020
 
Time:  9.30AM - 11.00 AM
 
Location: 3105 Lambton Tower

Abstract:

 
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 whereas Drug Repurposing involves finding indications for drugs that are in investigational state. 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 for one of the most prevailing cancer diseases, the Breast Cancer using the existing Protein-Protein interaction (PPI) networks. The proposed method is based on the idea that if a perturbation gene expression profile inversely corelates 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 Gene Expression Omnibus. The perturbation gene expression data corresponding to MCF7 cell line was extracted from the NIH, LINCS dataset. Machine Learning techniques are used to select the best suited drug for the breast cancer disease. We have used a combinatorial optimization algorithm to obtain a ranked list of suitable drug candidates and our algorithm finds 8 out of the 10 approved breast cancer drugs.

 

Thesis Committee: 

Internal Reader: Dr. Xiaobu Yuan
External Reader: Dr. Myron Hlynka
Advisor: Dr. Alioune Ngom
Chair: Dr. Sherif Saad
 
 

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