Abstract
dc:description.abstractThe rapid evolution of omics technologies has revolutionized drug target discovery, transitioning from phenotypic and function-centric methodologies to more mechanistic and target-driven approaches. To maximise drug development success at earlier phases, understanding the role of targets in disease mechanisms is crucial for accelerating drug development and identifying beneficiary patient cohorts. Although Machine Learning-based approaches unveil cryptic patterns from biological data, their application in smaller scale research is hindered by limitations in data availability and quality. This thesis addresses these challenges by exploring various approaches, with a focus extends to unravel disease mechanisms from omics data analysis. Firstly, an assessment of methods for data processing was conducted, focusing on metabolomics and transcriptomics data. This chapter also evaluated conventional approaches in the analyses of differential abundance and pathway enrichment. Secondly, the exploration extended into the utilization of publicly available networks for studying disease mechanisms during treatment with a case study in Chronic Myeloid Leukaemia (CML). This chapter not only unveiled pathways integral to CML biology under novel combination therapies but also identified more promising targets. Thirdly, the utility of time-series untargeted metabolomics in studying ischaemia reperfusion injury was investigated. Employing various models, the study characterized mechanistic metabolic pathways and enzymes that exhibit differential expression across human, pig, and mouse hearts and across ischaemia-prone tissues. The last result chapter delved into the impact of a specific target on alternative splicing in gastric cancer. This chapter illustrated how both extensive and compact transcriptomic datasets can be utilized to investigate disease mechanisms at the level of splicing, thereby enhancing our comprehension of disease prognosis. In summary, this thesis leveraged data-driven approaches with omics as a pivotal modality to derive biological insights into the explored diseases. The work presented not only offers effective and efficient solutions for unravelling disease mechanisms using different omics datasets but also provides actionable insights for drug development, thereby advancing the pathway for the discovery and validation of novel therapeutics targets.
Degree
thesis:*- Name dc:type.qualificationname
- Doctor of Philosophy (PhD)
- Level dc:type.qualificationlevel
- Doctoral
- Grantor dc:publisher.institution
- University of Cambridge
- Year dc:date.issued
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Lei, Cheng Wai
- Advisors dc:contributor.advisor
-
- Saeb-Parsy, Kourosh
- Han, Namshik
Subjects
dc:subject × 2Rights
dc:rightsIdentifiers
dc:identifier.*- DOI dc:identifier.doi
- https://doi.org/10.17863/CAM.112610
- OAI identifier oai:identifier
- oai:www.repository.cam.ac.uk:1810/374595