Chen, Weiqi (2018). Active module identification in biological networks. University of Birmingham. Ph.D.
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Chen18PhD.pdf
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Abstract
This thesis addresses the problem of active module identification in biological networks. Active module
identification is a research topic in network biology that aims to identify regions in network showing striking
changes in activity. It is often associated with a given cellular response and expected to reveal dynamic and
process-specific information.
The key research questions for this thesis are the practical formulations of active module identification problem,the design of effective, efficient and robust algorithms to identify active modules, and the right way to interpret identified active module.
This thesis contributes by proposing three different algorithm frameworks to address the research question from three different aspects. It first explores an integrated approach of combining both gene differential expression and differential correlation, formulates it as a multi-objective problem, and solves it on both simulated data and real world data. Then the thesis investigates a novel approach that brings in prior knowledge of biological process, and balances between pure data-driven search and prior information guidance. Finally, the thesis presents a brand new framework of identifying active module and topological communities simultaneously using evolutionary multitasking, accompanied with a series of task-specific algorithm designs and improvements, and provides a new way of integrating topological information to help the interpretation of active module.
Type of Work: | Thesis (Doctorates > Ph.D.) | |||||||||
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Award Type: | Doctorates > Ph.D. | |||||||||
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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 Q Science > QH Natural history > QH301 Biology |
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URI: | http://etheses.bham.ac.uk/id/eprint/8542 |
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