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Computational Toxicology

Overview of attention for book
Cover of 'Computational Toxicology'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach
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    Chapter 2 The OECD QSAR Toolbox Starts Its Second Decade
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    Chapter 3 QSAR: What Else?
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    Chapter 4 (Q)SARs as Adaptations to REACH Information Requirements
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    Chapter 5 Machine Learning Methods in Computational Toxicology
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    Chapter 6 Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling
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    Chapter 7 Molecular Similarity in Computational Toxicology
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    Chapter 8 Molecular Docking for Predictive Toxicology
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    Chapter 9 Criteria and Application on the Use of Nontesting Methods within a Weight of Evidence Strategy
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    Chapter 10 Characterization and Management of Uncertainties in Toxicological Risk Assessment: Examples from the Opinions of the European Food Safety Authority
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    Chapter 11 Computational Toxicology and Drug Discovery
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    Chapter 12 Approaching Pharmacological Space: Events and Components
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    Chapter 13 Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation
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    Chapter 14 Enalos Suite: New Cheminformatics Platform for Drug Discovery and Computational Toxicology
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    Chapter 15 Ion Channels in Drug Discovery and Safety Pharmacology
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    Chapter 16 Computational Approaches in Multitarget Drug Discovery
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    Chapter 17 Nanoformulations for Drug Delivery: Safety, Toxicity, and Efficacy
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    Chapter 18 Toxicity Potential of Nutraceuticals
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    Chapter 19 Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
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    Chapter 20 (Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks
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    Chapter 21 Stem Cell-Based Methods to Predict Developmental Chemical Toxicity
  23. Altmetric Badge
    Chapter 22 Predicting Chemically Induced Skin Sensitization by Using In Chemico / In Vitro Methods
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    Chapter 23 Hepatotoxicity Prediction by Systems Biology Modeling of Disturbed Metabolic Pathways Using Gene Expression Data
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    Chapter 24 Nontest Methods to Predict Acute Toxicity: State of the Art for Applications of In Silico Methods
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    Chapter 25 Predictive Systems Toxicology
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    Chapter 26 Chemoinformatic Approach to Assess Toxicity of Ionic Liquids
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    Chapter 27 Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results
Attention for Chapter 23: Hepatotoxicity Prediction by Systems Biology Modeling of Disturbed Metabolic Pathways Using Gene Expression Data
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Chapter title
Hepatotoxicity Prediction by Systems Biology Modeling of Disturbed Metabolic Pathways Using Gene Expression Data
Chapter number 23
Book title
Computational Toxicology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7899-1_23
Pubmed ID
Book ISBNs
978-1-4939-7898-4, 978-1-4939-7899-1
Authors

Oriol López-Massaguer, Manuel Pastor, Ferran Sanz, Pablo Carbonell, López-Massaguer, Oriol, Pastor, Manuel, Sanz, Ferran, Carbonell, Pablo

Abstract

The present method describes a systems biology approach for the in silico predictive modeling of drug toxicity. The data from LINCS were used to determine the type and number of pathways disturbed by each compound and to estimate the extent of disturbance (network perturbation elasticity). Moreover, the most frequently disturbed metabolic pathways and reactions were determined across the studied toxicants. The process was exemplified by successful predictions on various statins. In conclusion, an entirely new approach linking gene expression alterations to the prediction of complex organ toxicity was developed.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Student > Postgraduate 2 17%
Researcher 2 17%
Professor 1 8%
Student > Bachelor 1 8%
Other 0 0%
Unknown 3 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 17%
Pharmacology, Toxicology and Pharmaceutical Science 2 17%
Environmental Science 1 8%
Agricultural and Biological Sciences 1 8%
Chemistry 1 8%
Other 1 8%
Unknown 4 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 August 2018.
All research outputs
#10,605,801
of 13,322,622 outputs
Outputs from Methods in molecular biology
#4,358
of 8,472 outputs
Outputs of similar age
#200,308
of 268,174 outputs
Outputs of similar age from Methods in molecular biology
#1
of 1 outputs
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