| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000436442 |
| 005 | | 20200228143809 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781088315019 |
| 035 | |
▼a (MiAaPQ)AAI13808535 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Wang, Bo. |
| 245 | 10 |
▼a Integrating Genetic and Structural Features: Building a Hybrid Physical-Statistical Classifier for Variants Related to Protein-Drug Interactions. |
| 260 | |
▼a [S.l.]:
▼b Yale University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 99 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-03, Section: B. |
| 500 | |
▼a Advisor: Gerstein, Mark B. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Yale University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a A key issue in drug design is understanding how population variation affects drug efficacy by altering binding affinity (BA) in different individuals -- an important consideration for pharmaceutical regulators. Ideally, we would like to evaluate millions of single-nucleotide variants (SNVs) in relation to BA. However, only hundreds of protein-drug complexes with BA and mutations are available, constituting too small a gold-standard for straightforward statistical model training. Thus, we take a hybrid approach using physically-based calculations to bootstrap the parameterization of a full statistical model. In particular, we do 3D-structure-based docking calculations on ~10,000 SNVs modifying known protein-drug complexes to construct a pseudo-gold-standard dataset of BAs. Then we develop a complete statistical model combining structure, ligand and sequence features and show how it can be applied to score millions of SNVs. Finally, we show our model has good performance in cross-validated testing (AUROC of 97%) and can also be validated by orthogonal ligand-binding data. |
| 590 | |
▼a School code: 0265. |
| 650 | 4 |
▼a Bioinformatics. |
| 650 | 4 |
▼a Biophysics. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0715 |
| 690 | |
▼a 0786 |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Yale University.
▼b Chemistry. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-03B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0265 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15490547
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |