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- W2899255576 abstract "This thesis addresses the problem of active module identification in biological networks. Active moduleidentification is a research topic in network biology that aims to identify regions in network showing strikingchanges in activity. It is often associated with a given cellular response and expected to reveal dynamic andprocess-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." @default.
- W2899255576 created "2018-11-09" @default.
- W2899255576 creator A5023022034 @default.
- W2899255576 date "2018-12-01" @default.
- W2899255576 modified "2023-09-23" @default.
- W2899255576 title "Active module identification in biological networks" @default.
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