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Integrating Genetic and Structural F...

Wang, Bo.

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Integrating Genetic and Structural Features: Building a Hybrid Physical-Statistical Classifier for Variants Related to Protein-Drug Interactions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Integrating Genetic and Structural Features: Building a Hybrid Physical-Statistical Classifier for Variants Related to Protein-Drug Interactions./
Author:	Wang, Bo.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	99 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Contained By:	Dissertations Abstracts International81-03B.
Subject:	Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13808535
ISBN:	9781088315019

Integrating Genetic and Structural Features: Building a Hybrid Physical-Statistical Classifier for Variants Related to Protein-Drug Interactions.
Wang, Bo.

Integrating Genetic and Structural Features: Building a Hybrid Physical-Statistical Classifier for Variants Related to Protein-Drug Interactions. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 99 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: B.

Thesis (Ph.D.)--Yale University, 2019.

This item must not be sold to any third party vendors.

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.

ISBN: 9781088315019Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Human genome

Integrating Genetic and Structural Features: Building a Hybrid Physical-Statistical Classifier for Variants Related to Protein-Drug Interactions.
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300 $a 99 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
500 $a Advisor: Gerstein, Mark B.
502 $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.
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653 $a Human genome
653 $a Precise medicine
653 $a Protein-drug binding
653 $a Random forest
653 $a Snv
653 $a Supervised learning
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710 2 $a Yale University. $b Chemistry. $3 2101664
773 0 $t Dissertations Abstracts International $g 81-03B.
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