Zhang, Han (2022). Graph-based machine learning and its application on multi-omics data analysis. University of Birmingham. Ph.D.
Zhang2022PhD.pdf
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Abstract
With the development of omics technologies, there is a large amount of biological data available. The biological data is usually complex, ill-sampled, and high dimensional. As a result, gaining insightful knowledge from the biological data is still a challenging problem. Many of those biological data can be represented using graphs. Over the years, many graph-based machine learning methods have been developed to analyse graphs in many tasks, such as module detection, feature engineering, and link prediction. This thesis provides the applications of graph-based machine learning methods on three types of biological data with the following contributions:
• A novel method that allows overlapping between the peak modules
in the metabolite annotation problem on liquid chromatography-mass
spectrometry untargeted data.
• A novel method that allows high freedom of the shapes of the topological modules in the detection of network biomarkers for gene expression
data.
• A novel method that incorporates the implicit networks constructed
from the drug-target interaction network in the drug-target interaction
prediction problem.
With these contributions, we can gain more understanding of the biological
data.
Type of Work: | Thesis (Doctorates > Ph.D.) | ||||||||||||
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Award Type: | Doctorates > Ph.D. | ||||||||||||
Supervisor(s): |
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Licence: | All rights reserved | ||||||||||||
College/Faculty: | Colleges (2008 onwards) > College of Engineering & Physical Sciences | ||||||||||||
School or Department: | School of Computer Science | ||||||||||||
Funders: | None/not applicable | ||||||||||||
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science | ||||||||||||
URI: | http://etheses.bham.ac.uk/id/eprint/12534 |
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