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020 ▼a 9780438377455
035 ▼a (MiAaPQ)AAI10840439
035 ▼a (MiAaPQ)duke:14839
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 310
1001 ▼a Larson, Gary J.
24510 ▼a Advances in Bayesian Modeling of Protein Structure Evolution.
260 ▼a [S.l.] : ▼b Duke University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 143 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
500 ▼a Adviser: Scott C. Schmidler.
5021 ▼a Thesis (Ph.D.)--Duke University, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0066.
650 4 ▼a Statistics.
650 4 ▼a Biology.
690 ▼a 0463
690 ▼a 0306
71020 ▼a Duke University. ▼b Statistical Science.
7730 ▼t Dissertation Abstracts International ▼g 80-02B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0066
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999724 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자