Machine learning approaches to predict drug responses in cancer from multi-omics data
Cancer is a complex disease that involves genetic mutations and diverse tumour behaviour and characteristics. With its complexities, there comes major challenges when it comes to treating cancer such as requiring personalised treatment. Therefore, it is important for medical experts to have a det...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/171943 |
| _version_ | 1811679567587311616 |
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| author | Muhammad Zaki Bin Mohammad Bakri |
| author2 | Jagath C Rajapakse |
| author_facet | Jagath C Rajapakse Muhammad Zaki Bin Mohammad Bakri |
| author_sort | Muhammad Zaki Bin Mohammad Bakri |
| collection | NTU |
| description | Cancer is a complex disease that involves genetic mutations and diverse tumour behaviour and
characteristics. With its complexities, there comes major challenges when it comes to treating
cancer such as requiring personalised treatment. Therefore, it is important for medical experts
to have a detailed understanding of patients’ cancer cells to be able to administer medicinal
efforts effectively.
In this day and age there is an abundance of data which also includes the various omics data of
cancer cells. With these omics data and integrating them together, medical experts can analyse
the relationships between each omics and obtain new insights into each biological component
during stages of cancer. This can help in understanding cancer cells as well as improving the
personalised treatment of cancer.
In this project, our end goal was to predict drug responses of cancer cell lines from multi-omics
data. However, multi-omics data has high dimensions which makes it difficult for integration
and analysis. Hence the approach we have taken to tackle this high dimensionality issue was
by implementing a dimension reduction technique using Variational Autoencoders (VAE).
Various integration techniques were also explored. Afterwards, a deep neural network predictor
was built to predict drug responses of cancer cells.
With this predictor, this will help in future drug and cancer research as well as improve current
cancer treatment. |
| first_indexed | 2024-10-01T03:11:13Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/171943 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:11:13Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1719432023-11-17T15:37:07Z Machine learning approaches to predict drug responses in cancer from multi-omics data Muhammad Zaki Bin Mohammad Bakri Jagath C Rajapakse School of Computer Science and Engineering ASJagath@ntu.edu.sg Engineering::Computer science and engineering Cancer is a complex disease that involves genetic mutations and diverse tumour behaviour and characteristics. With its complexities, there comes major challenges when it comes to treating cancer such as requiring personalised treatment. Therefore, it is important for medical experts to have a detailed understanding of patients’ cancer cells to be able to administer medicinal efforts effectively. In this day and age there is an abundance of data which also includes the various omics data of cancer cells. With these omics data and integrating them together, medical experts can analyse the relationships between each omics and obtain new insights into each biological component during stages of cancer. This can help in understanding cancer cells as well as improving the personalised treatment of cancer. In this project, our end goal was to predict drug responses of cancer cell lines from multi-omics data. However, multi-omics data has high dimensions which makes it difficult for integration and analysis. Hence the approach we have taken to tackle this high dimensionality issue was by implementing a dimension reduction technique using Variational Autoencoders (VAE). Various integration techniques were also explored. Afterwards, a deep neural network predictor was built to predict drug responses of cancer cells. With this predictor, this will help in future drug and cancer research as well as improve current cancer treatment. Bachelor of Engineering (Computer Science) 2023-11-17T03:19:05Z 2023-11-17T03:19:05Z 2023 Final Year Project (FYP) Muhammad Zaki Bin Mohammad Bakri (2023). Machine learning approaches to predict drug responses in cancer from multi-omics data. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171943 https://hdl.handle.net/10356/171943 en SCSE22-1035 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Muhammad Zaki Bin Mohammad Bakri Machine learning approaches to predict drug responses in cancer from multi-omics data |
| title | Machine learning approaches to predict drug responses in cancer from multi-omics data |
| title_full | Machine learning approaches to predict drug responses in cancer from multi-omics data |
| title_fullStr | Machine learning approaches to predict drug responses in cancer from multi-omics data |
| title_full_unstemmed | Machine learning approaches to predict drug responses in cancer from multi-omics data |
| title_short | Machine learning approaches to predict drug responses in cancer from multi-omics data |
| title_sort | machine learning approaches to predict drug responses in cancer from multi omics data |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/171943 |
| work_keys_str_mv | AT muhammadzakibinmohammadbakri machinelearningapproachestopredictdrugresponsesincancerfrommultiomicsdata |