Date of Award

Summer 2021

Publication Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Drug repurposing, Hungarian algorithm, Perturbation gene expression, Protein-protein interaction (PPI) networks, Reporting Odds Ratio

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

In this thesis, we are interested in finding the best drugs that can be repurposed for the disease and able to find the adverse effects such drugs that are FDA-Approved. Developing an effective drug can be a time-consuming and expensive crucible method. 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. 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 the Louvain community detection algorithm on the breast network data and we obtain 14 communities. Then the correlation analysis was performed on each of the drug network data and the corresponding disease network communities. Finally, we do the Hungarian Algorithm and Edmond’s matching Algorithm to obtain the best-suited drug that could be repurposed for breast cancer disease, and using reporting odds ratio method, we could find out the adverse effects of drugs approved by the FDA.

Download

Share

COinS