Interpretation of genetic regulatory networks using protein interaction and transcription information단백질 상호작용과 전사정보를 이용한 유전자 조절 네트워크 해석

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dc.contributor.advisorLee, Do-Heon-
dc.contributor.advisor이도헌-
dc.contributor.authorKim, Sang-Woo-
dc.contributor.author김상우-
dc.date.accessioned2011-12-12T07:28:03Z-
dc.date.available2011-12-12T07:28:03Z-
dc.date.issued2004-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=240415&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/27094-
dc.description학위논문(석사) - 한국과학기술원 : 바이오시스템학과, 2004.8, [ v, 22 p. ]-
dc.description.abstractConstructing entire genetic regulatory networks is an ambitious goal for understanding and representing living mechanisms of a cell. Although several large scale experiments have tried to infer a lot of relations between genes, most of the results do not represent direct causal relations of genes, such as transcriptional activation or repression, but a set of obscure relations which rely upon the statistical or structural probability of the applied learning algorithms. These obscure relations are believed to have a lot of false positives and hardly interpreted to biological meanings of the target cell. To interpret and extract useful and reliable information from large scale regulatory networks, we developed an automated causal relation screening method for genetic regulatory networks by modeling biologically possible flows of proteins; a viaduct. In the experiment on a Bayesian genetic regulatory network of yeast cell cycle, we have found 821 viaducts by constructing a partial directed graph which consists of protein-protein interaction data and transcription factorinformation of Saccharomyces cerevisiae. Finding and inspecting those viaducts provides a new way to extract uncovered causal relations from large scale genetic regulatory networks and to measure how much the networks are constructed to contain reliable information.eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectPROTEIN-PROTEIN INTERACTION-
dc.subjectREGULATORY NETWORK-
dc.subjectTRANSCRIPTION REGULATION-
dc.subject전사정보-
dc.subject단백질 상호작용-
dc.subject유전자 조절 네트워크-
dc.titleInterpretation of genetic regulatory networks using protein interaction and transcription information-
dc.title.alternative단백질 상호작용과 전사정보를 이용한 유전자 조절 네트워크 해석-
dc.typeThesis(Master)-
dc.identifier.CNRN240415/325007 -
dc.description.department한국과학기술원 : 바이오시스템학과, -
dc.identifier.uid020023909-
dc.contributor.localauthorLee, Do-Heon-
dc.contributor.localauthor이도헌-
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