서브메뉴
검색
Model Based Visualization of Structure in Biological Data
Model Based Visualization of Structure in Biological Data
상세정보
- 자료유형
- 학위논문
- Control Number
- 0014999898
- International Standard Book Number
- 9780438371170
- Dewey Decimal Classification Number
- 574
- Main Entry-Personal Name
- Dey, Kushal K.
- Publication, Distribution, etc. (Imprint
- [Sl] : The University of Chicago, 2018
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2018
- Physical Description
- 130 p
- General Note
- Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
- General Note
- Adviser: Matthew Stephens.
- Dissertation Note
- Thesis (Ph.D.)--The University of Chicago, 2018.
- Summary, Etc.
- 요약Biological Data comes in varied forms and the scale of the data is typically large, which often necessitates distinct modeling frameworks and tools to process, analyze and visually summarize the data. An overarching theme of this doctoral thesis
- Summary, Etc.
- 요약The second chapter of this thesis extends the concept of a mixed membership model, popularly known as ADMIXTURE model in population genetics and topic model in Natural Language Processing (NLP), to the context of RNA-sequencing read expression
- Summary, Etc.
- 요약The third chapter extends similar mixed membership models to analyzing DNA damage patterns in ancient DNA (aDNA) samples, and explore and jointly summarize multiple aDNA samples together with modern samples. Applied to a combined data of modern
- Summary, Etc.
- 요약The visual summary of DNA damage patterns, depicted above, includes a version of logo plot that highlights enrichment and depletion of damage features with respect to a background level of mismatch features computed from modern individuals. We c
- Summary, Etc.
- 요약In the fifth chapter, we propose an adaptive method for shrinking correlation matrices that leads to a parsimonious representation of the underlying association structure between variables. This method is flexible in handling data matrices with
- Subject Added Entry-Topical Term
- Biostatistics
- Subject Added Entry-Topical Term
- Bioinformatics
- Subject Added Entry-Topical Term
- Genetics
- Added Entry-Corporate Name
- The University of Chicago Statistics
- Host Item Entry
- Dissertation Abstracts International. 80-01B(E).
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:553488
MARC
008190618s2018 c eng d■001000014999898
■00520190102172548
■020 ▼a9780438371170
■035 ▼a(MiAaPQ)AAI10843117
■035 ▼a(MiAaPQ)uchicago:14510
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aDey, Kushal K.
■24510▼aModel Based Visualization of Structure in Biological Data
■260 ▼a[Sl]▼bThe University of Chicago▼c2018
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2018
■300 ▼a130 p
■500 ▼aSource: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
■500 ▼aAdviser: Matthew Stephens.
■5021 ▼aThesis (Ph.D.)--The University of Chicago, 2018.
■520 ▼aBiological Data comes in varied forms and the scale of the data is typically large, which often necessitates distinct modeling frameworks and tools to process, analyze and visually summarize the data. An overarching theme of this doctoral thesis
■520 ▼aThe second chapter of this thesis extends the concept of a mixed membership model, popularly known as ADMIXTURE model in population genetics and topic model in Natural Language Processing (NLP), to the context of RNA-sequencing read expression
■520 ▼aThe third chapter extends similar mixed membership models to analyzing DNA damage patterns in ancient DNA (aDNA) samples, and explore and jointly summarize multiple aDNA samples together with modern samples. Applied to a combined data of modern
■520 ▼aThe visual summary of DNA damage patterns, depicted above, includes a version of logo plot that highlights enrichment and depletion of damage features with respect to a background level of mismatch features computed from modern individuals. We c
■520 ▼aIn the fifth chapter, we propose an adaptive method for shrinking correlation matrices that leads to a parsimonious representation of the underlying association structure between variables. This method is flexible in handling data matrices with
■590 ▼aSchool code: 0330.
■650 4▼aBiostatistics
■650 4▼aBioinformatics
■650 4▼aGenetics
■690 ▼a0308
■690 ▼a0715
■690 ▼a0369
■71020▼aThe University of Chicago▼bStatistics.
■7730 ▼tDissertation Abstracts International▼g80-01B(E).
■773 ▼tDissertation Abstract International
■790 ▼a0330
■791 ▼aPh.D.
■792 ▼a2018
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T14999898▼nKERIS
■980 ▼a201812▼f2019
미리보기
내보내기
chatGPT토론
Ai 추천 관련 도서
detalle info
- Reserva
- 캠퍼스간 도서대출
- 서가에 없는 책 신고
- Mi carpeta
