Identifying prognostic subgroups of luminal-A breast cancer using denoising autoencoders = 디노이징 오토인코더를 이용한 luminal-A 아형 유방암의 예후적 하위 그룹 식별

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dc.contributor.advisorLee, Doheon-
dc.contributor.advisor이도헌-
dc.contributor.authorWang, Seunghyun-
dc.date.accessioned2021-05-12T19:33:42Z-
dc.date.available2021-05-12T19:33:42Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=909921&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/283840-
dc.description학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2020.2,[v, 50 p. :]-
dc.description.abstractLuminal-A breast cancer is a subtype with the largest number of patients, about 40% of all breast cancer patients. The biggest characteristic of luminal-A breast cancer patients is a wide range of variation in prognosis for endocrine therapy. Therefore, this research divides the luminal-A breast cancer patients into the two distinct prognostic subgroups. The latent features generated through denoising autoencoders that extract and compress gene expression patterns of luminal-A breast cancer patients identify the two prognostic subgroups. The significance difference in overall survival between two subgroups are shown via log-rank test that is a hypothesis test to compare the survival distributions of two samples. In addition, through biological pathway analysis, it is found that the autophagy-lysosome pathways are more activated in the better prognostic subgroups. It is expected that this research can be used for personalized breast cancer treatment.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectbreast cancer▼aautoencoders▼aprognosis▼asubgroups▼aautophagy-
dc.subject유방암▼a오토인코더▼a예후▼a하위그룹▼a자가포식-
dc.titleIdentifying prognostic subgroups of luminal-A breast cancer using denoising autoencoders = 디노이징 오토인코더를 이용한 luminal-A 아형 유방암의 예후적 하위 그룹 식별-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :바이오및뇌공학과,-
dc.contributor.alternativeauthor왕승현-
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