Topological motif-based bio-network analysis = 위상학적 모티프 기반 바이오 네트워크 분석

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Recently, network motif (or graphlet) properties have been widely utilized as important topological features of bio-networks. Network motifs are recurrent and statistically significant partial subgraphs or patterns. In this thesis, we analyzed various bio-networks based on topological property of network motifs. In part I, we developed Typed Network Motif Comparison Algorithm (TNMCA) for repositioning drugs using topological properties of given networks. TNMCA is a powerful inference algorithm for multi-level biomedical interaction data as the algorithm depends on the different types of entities and relations. In part II, we propose a new network model incorporating grouped attachment (GA) and apply it to real-world networks. Corresponding networks generated by GA model showed a higher similarity of motif properties with real-world networks compared to corresponding networks generated by existing network models.
Advisors
Lee, Doheonresearcher이도헌researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2017.2,[v, 75 p. :]

Keywords

topological motif; network motif; bio-network analysis; drug repositioning; network model; 위상학적 모티프; 네트워크 모티프; 바이오네트워크 분석; 약물 재창출; 네트워크 모델

URI
http://hdl.handle.net/10203/241802
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675697&flag=dissertation
Appears in Collection
BiS-Theses_Ph.D.(박사논문)
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