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Simultaneous Analysis of Large Scale Datasets in Different ChIP-seq Problem Settings
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Simultaneous Analysis of Large Scale Datasets in Different ChIP-seq Problem Settings. |
| 개인저자 | Chen, Kailei. |
| 단체저자명 | The University of Wisconsin - Madison. Statistics. |
| 발행사항 | [S.l.]: The University of Wisconsin - Madison., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 85 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438403796 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Wisconsin - Madison, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Sunduz Keles. |
| 요약 | Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a common genomics tool for studying regulation of transcription. Analyses of ChIP-seq data typically concern: i) binding state inference, which aims at detecting the DNA loci occ |
| 요약 | Chapter 2 introduces a MAP-based Asymptotic Derivations from Bayes (MAD-Bayes) method based on strong assumptions in MBASIC framework. This results in a K-means-like optimization algorithm which converges rapidlyThe fast-converging nature enable |
| 요약 | In Chapter 3, I extends the MAD-Bayes MBASIC to the allele-specific analysis setting, via a variance-stabilizing transformation, enabling the method to apply discrete distributions in both binding state and allele-specific binding problems. Appl |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
