DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yi, Gwan-Su | - |
dc.contributor.advisor | 이관수 | - |
dc.contributor.author | Lee, Yoon Hyeok | - |
dc.date.accessioned | 2023-06-21T19:34:19Z | - |
dc.date.available | 2023-06-21T19:34:19Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1006512&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308036 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2021.2,[vii, 102 p. :] | - |
dc.description.abstract | Recently, precision medicine research on type 2 diabetes has been actively conducted, but currently, studies on clustering of patient groups using clinical variables are dominant, and stratification of type 2 diabetes patient groups based on disease mechanisms is still difficult. In this study, to solve this problem, the mechanisms of type 2 diabetes were first defined and hierarchically structured to enable bioinformatics analysis. In addition, I developed the S-cube, a method for discovering the patient groups and their corresponding disease mechanisms from a limited number of transcriptome data. S-cube is a method of discovering each patient group and mechanism by creating combinations of various patients and discriminating data through expert-based prediction principles. As a result of comparing the classification performance with existing methods such as SVM and Pasting, it was confirmed that the S-cube showed higher classification performance. In addition, 21 patient groups were successfully discovered by applying the S-cube method to the type 2 diabetes pancreatic islet transcriptome data. Finally, a surrogate experimental model was constructed to predict drug response in the discovered patient group. The series of procedures including S-cube proposed in this study can be applied not only to type 2 diabetes but also to other diseases. Therefore, it is expected that it will be able to contribute to the application of disease mechanism-based precision medicine of other diseases like rare diseases. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Type 2 diabetes▼aPrecision medicine▼aMechanism-specific subgroups▼aTranscriptome▼aPancreatic islets▼aPancreatic beta-cell▼aPrior disease information integration▼aSurrogate experimental model▼aHierarchical structure of disease mechanism▼aRandom subsampling | - |
dc.subject | 제2형 당뇨▼a정밀의학▼a기전 맞춤형 환자군▼a전사체▼a췌장섬▼a췌장 베타세포▼a사전질병정보 통합▼a대체 실험 모델▼a질병 기전 계층적 구조화▼a무작위 서브 샘플링 | - |
dc.title | Development of random subsample classifiers identifying mechanism-specific subgroups of type 2 diabetes | - |
dc.title.alternative | 무작위 서브 샘플링 기반 제2형 당뇨 기전 맞춤형 환자 그룹 분류 기술 개발 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | 이윤혁 | - |
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