Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171943
Title: Machine learning approaches to predict drug responses in cancer from multi-omics data
Authors: Muhammad Zaki Bin Mohammad Bakri
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Publisher: Nanyang Technological University
Source: 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
Project: SCSE22-1035 
Abstract: 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.
URI: https://hdl.handle.net/10356/171943
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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