Predicting the influence of cancer drugs on human signalling network
Cancer is one of the largest disease burden in the world, being the second cause of death in people worldwide. Traditional methods such as radiotherapy and chemotherapy have been the mainstay of cancer treatment for decades. However, such non-targeted therapy methods have been known to cause severe...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175192 |
| _version_ | 1811696733793550336 |
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| author | Teo, I-Jen |
| author2 | Sourav S Bhowmick |
| author_facet | Sourav S Bhowmick Teo, I-Jen |
| author_sort | Teo, I-Jen |
| collection | NTU |
| description | Cancer is one of the largest disease burden in the world, being the second cause of death in people worldwide. Traditional methods such as radiotherapy and chemotherapy have been the mainstay of cancer treatment for decades. However, such non-targeted therapy methods have been known to cause severe side-effects. Targeted therapy has emerged as a promising approach to treat cancer, while promising to reduce side-effects. Targeted therapy targets genes in the human signalling network, which presents the opportunity to predict the influence of drug targets on the signalling network using graph traversal algorithms and other novel frameworks. Hence, this project proposes to implement such methods in a user-friendly and interactive graphical user interface for users to intuitively visualise the effects of a set of drug targets on the signalling network. |
| first_indexed | 2024-10-01T07:44:04Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175192 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:44:04Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1751922024-04-19T15:42:43Z Predicting the influence of cancer drugs on human signalling network Teo, I-Jen Sourav S Bhowmick School of Computer Science and Engineering ASSourav@ntu.edu.sg Computer and Information Science Cancer is one of the largest disease burden in the world, being the second cause of death in people worldwide. Traditional methods such as radiotherapy and chemotherapy have been the mainstay of cancer treatment for decades. However, such non-targeted therapy methods have been known to cause severe side-effects. Targeted therapy has emerged as a promising approach to treat cancer, while promising to reduce side-effects. Targeted therapy targets genes in the human signalling network, which presents the opportunity to predict the influence of drug targets on the signalling network using graph traversal algorithms and other novel frameworks. Hence, this project proposes to implement such methods in a user-friendly and interactive graphical user interface for users to intuitively visualise the effects of a set of drug targets on the signalling network. Bachelor's degree 2024-04-19T13:05:21Z 2024-04-19T13:05:21Z 2024 Final Year Project (FYP) Teo, I. (2024). Predicting the influence of cancer drugs on human signalling network. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175192 https://hdl.handle.net/10356/175192 en application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Teo, I-Jen Predicting the influence of cancer drugs on human signalling network |
| title | Predicting the influence of cancer drugs on human signalling network |
| title_full | Predicting the influence of cancer drugs on human signalling network |
| title_fullStr | Predicting the influence of cancer drugs on human signalling network |
| title_full_unstemmed | Predicting the influence of cancer drugs on human signalling network |
| title_short | Predicting the influence of cancer drugs on human signalling network |
| title_sort | predicting the influence of cancer drugs on human signalling network |
| topic | Computer and Information Science |
| url | https://hdl.handle.net/10356/175192 |
| work_keys_str_mv | AT teoijen predictingtheinfluenceofcancerdrugsonhumansignallingnetwork |