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Advances in Bayesian Modeling of Protein Structure Evolution
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Advances in Bayesian Modeling of Protein Structure Evolution. |
| 개인저자 | Larson, Gary J. |
| 단체저자명 | Duke University. Statistical Science. |
| 발행사항 | [S.l.]: Duke University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 143 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438377455 |
| 학위논문주기 | Thesis (Ph.D.)--Duke University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Scott C. Schmidler. |
| 요약 | This thesis contributes to a statistical modeling framework for protein sequence and structure evolution. An existing Bayesian model for protein structure evolution is extended in two unique ways. Each of these model extensions addresses an impo |
| 요약 | Most available models for protein structure evolution do not model interdependence between the backbone sites of the protein, yet the assumption that the sites evolve independently is known to be false. I argue that ignoring such dependence lead |
| 요약 | The second model expansion allows for evolutionary inference on protein pairs having structural discrepancies attributable to backbone flexion. Thus, the model expansion exposes flexible protein structures to the capabilities of Bayesian protein |
| 요약 | Finally, I present work related to the study of bias in site-independent models for sequence evolution. In the case of binary sequences, I discuss strategies for theoretical proof of bias and provide various details to that end, including detail |
| 일반주제명 | Statistics. Biology. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
