Computational modelling for breast cancer prognosis in precision medicine /
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(Published version)
Date
2022
Authors
Li, Xiaomei
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Type:
thesis
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Abstract
Understanding tumour heterogeneity is fundamental for improving breast cancer prognosis and working towards precision medicine. In this thesis, we develop computational methods and a software tool to address the following research questions posed by breast cancer heterogeneity: 1) how to develop a breast cancer prognosis method with stable prediction performance in different independent breast cancer cohorts; 2) how to examine the roles of miRNAs and lncRNAs in characterising breast cancer outcomes and subtypes; 3) how to utilise single-cell data to identify molecular signatures related to intra-tumour heterogeneity to improve breast cancer prognosis. The experiments on real breast cancer datasets demonstrated that the computational methods presented in the thesis outperformed the existing methods and provided new biological knowledge.
School/Discipline
University of South Australia. UniSA STEM
UniSA STEM
UniSA STEM
Dissertation Note
Thesis (PhD(Computer and Information Science)--University of South Australia, 2022.
Provenance
Copyright 2022 Xiaomei Li
Description
1 ethesis (xx,193 pages) :
colour illustrations.
Includes bibliographical references (pages 152-193)
colour illustrations.
Includes bibliographical references (pages 152-193)
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506 0#$fstar $2Unrestricted online access