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Statistical Tools in Early-Stage Drug Discovery
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Statistical Tools in Early-Stage Drug Discovery. |
| 개인저자 | Zhang, Huikun. |
| 단체저자명 | The University of Wisconsin - Madison. Statistics. |
| 발행사항 | [S.l.]: The University of Wisconsin - Madison., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 91 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438254879 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Wisconsin - Madison, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Michael A. Newton. |
| 요약 | In biomedical research, drug discovery is usually done through studying the interaction between drug-like compounds and protein targets. The challenge is that it is inefficient to screen millions of compounds. Computational tools have been deployed to save the screening effort. |
| 요약 | In this collaborated research with UW Small Molecule Screening Facility, two projects are focused: Consensus Docking: statistical models are developed using computational docking data to predict compound-target interactions; Informer compound set generation and prediction: prediction on compound-target interaction is made through using experimental assay data. |
| 요약 | Statistical considerations include mixture modeling, ranking and regression. In both study, improved drug discovery performance has been achieved through applying developed statistical models. |
| 일반주제명 | Statistics. Biochemistry. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
