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High-Throughput Screening Assays in Toxicology

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Cover of 'High-Throughput Screening Assays in Toxicology'

Table of Contents

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    Book Overview
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    Chapter 1 Monitoring Ligand-Activated Protein–Protein Interactions Using Bioluminescent Resonance Energy Transfer (BRET) Assay
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    Chapter 2 Mitochondrial Membrane Potential Assay
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    Chapter 3 High-Throughput Screening Assays in Toxicology
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    Chapter 4 Quantitative High-Throughput Luciferase Screening in Identifying CAR Modulators
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    Chapter 5 Transactivation and Coactivator Recruitment Assays for Measuring Farnesoid X Receptor Activity
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    Chapter 6 Cell-Based Assay for Identifying the Modulators of Antioxidant Response Element Signaling Pathway
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    Chapter 7 Study Liver Cytochrome P450 3A4 Inhibition and Hepatotoxicity Using DMSO-Differentiated HuH-7 Cells
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    Chapter 8 Determination of Histone H2AX Phosphorylation in DT40 Cells
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    Chapter 9 High-Throughput and High-Content Micronucleus Assay in CHO-K1 Cells
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    Chapter 10 High-Throughput Screening Assays in Toxicology
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    Chapter 11 High-Throughput Screening Assays in Toxicology
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    Chapter 12 A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling
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    Chapter 13 Correction of Microplate Data from High-Throughput Screening
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    Chapter 14 CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints
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    Chapter 15 Accounting Artifacts in High-Throughput Toxicity Assays
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    Chapter 16 Accessing the High-Throughput Screening Data Landscape
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    Chapter 17 Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling
Attention for Chapter 16: Accessing the High-Throughput Screening Data Landscape
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Chapter title
Accessing the High-Throughput Screening Data Landscape
Chapter number 16
Book title
High-Throughput Screening Assays in Toxicology
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-6346-1_16
Pubmed ID
Book ISBNs
978-1-4939-6344-7, 978-1-4939-6346-1
Authors

Daniel P. Russo, Hao Zhu, Russo, Daniel P., Zhu, Hao

Abstract

The progress of high-throughput screening (HTS) techniques is changing the chemical data landscape by producing massive biological data from tested compounds. Public data repositories (e.g., PubChem) receive HTS data provided by various institutes and this data pool is being updated on a daily basis. The goal of these data sharing efforts is to let users quickly obtain the biological data of target compounds. Without a universal chemical identifier, the repositories (e.g., PubChem) provide users various methods to query and retrieve chemical properties and biological data by several different chemical identifiers (e.g., SMILES, InChIKey, and IUPAC name). The major challenge for most users, especially computational modelers, is obtaining the biological data for a large dataset of compounds (e.g., thousands of drug molecules) instead of a single compound. This chapter aims to introduce the steps to access the public data repositories for target compounds with specific emphasis on the automatic data downloading for large datasets.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 21%
Student > Bachelor 2 14%
Student > Postgraduate 2 14%
Student > Ph. D. Student 2 14%
Other 1 7%
Other 2 14%
Unknown 2 14%
Readers by discipline Count As %
Medicine and Dentistry 3 21%
Agricultural and Biological Sciences 2 14%
Biochemistry, Genetics and Molecular Biology 2 14%
Social Sciences 1 7%
Neuroscience 1 7%
Other 0 0%
Unknown 5 36%