Omics analysis in understanding disease mechanisms
type: CreativeWork
description: The 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-d…
abstract: The 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-d…
author: Lei, Cheng Wai
language: eng
keywords: Bioinformatics, Computational Biology
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/075f202c-7d92-452f-8876-2fcbeb0d215b/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2024-04-08
date modified: 2026-07-22T22:24:14Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.112610