Dissertation
Development and application of systems biology approaches for multiple dimensional data integration on cancer genomics data
University of the Sunshine Coast, Queensland
Doctor of Philosophy, University of the Sunshine Coast, Queensland
2021
DOI:
https://doi.org/10.25907/00022
Appears in Cancer Research Cluster Research Collection
Abstract
Cancer is a genetic disorder classified by the accumulation of somatic cellular aberrations. During the last decade, many different techniques have been developed to comprehensively characterise these changes in cancer cells. Both large-scale and focused efforts using various bioinformatics tools have identified new targets in translational clinical research. These approaches have become an important component of the drug discovery and development of personalized medicine for the future. Thus, the complication of somatic genomic alterations in cancer genomes requires the development of robust computational framework and methods for the investigation of the biological function of genes. The aim of this study was to develop bioinformatics framework to perform an integrated analysis of large-scale cancer genomics data, as well as to demonstrate the applicability of these approaches in targeted cancer treatment research.
Details
- Title
- Development and application of systems biology approaches for multiple dimensional data integration on cancer genomics data
- Authors
- Yong Kiat Wee
- Contributors
- Min Zhao (Supervisor) - University of the Sunshine Coast, Queensland, School of Science and Engineering - Legacy
- Awarding institution
- University of the Sunshine Coast, Queensland
- Degree awarded
- Doctor of Philosophy
- Publisher
- University of the Sunshine Coast, Queensland
- DOI
- 10.25907/00022
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; Cancer Research Cluster; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99501008902621
- Output Type
- Dissertation
Metrics
21 File views/ downloads
153 Record Views