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Computational Chemogenomics

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Cover of 'Computational Chemogenomics'

Table of Contents

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    Book Overview
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    Chapter 1 A Survey of Web-Based Chemogenomic Data Resources
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    Chapter 2 Finding Potential Multitarget Ligands Using PubChem
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    Chapter 3 Fundamental Bioinformatic and Chemoinformatic Data Processing
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    Chapter 4 Parsing Compound–Protein Bioactivity Tables
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    Chapter 5 Impact of Molecular Descriptors on Computational Models
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    Chapter 6 Physicochemical Property Labels as Molecular Descriptors for Improved Analysis of Compound–Protein and Compound–Compound Networks
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    Chapter 7 Core Statistical Methods for Chemogenomic Data
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    Chapter 8 Structure-Based Detection of Orthosteric and Allosteric Pockets at Protein–Protein Interfaces
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    Chapter 9 Single Binding Pockets Versus Allosteric Binding
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    Chapter 10 Mapping Biological Activities to Different Types of Molecular Scaffolds: Exemplary Application to Protein Kinase Inhibitors
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    Chapter 11 SAR Matrix Method for Large-Scale Analysis of Compound Structure–Activity Relationships and Exploration of Multitarget Activity Spaces
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    Chapter 12 Linear and Kernel Model Construction Methods for Predicting Drug–Target Interactions in a Chemogenomic Framework
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    Chapter 13 Selection of Informative Examples in Chemogenomic Datasets
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    Chapter 14 A Platform for Comprehensive Genomic Profiling in Human Cancers and Pharmacogenomics Therapy Selection
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    Chapter 15 The Future of Computational Chemogenomics
Attention for Chapter 13: Selection of Informative Examples in Chemogenomic Datasets
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Chapter title
Selection of Informative Examples in Chemogenomic Datasets
Chapter number 13
Book title
Computational Chemogenomics
Published by
Humana Press, New York, NY, October 2018
DOI 10.1007/978-1-4939-8639-2_13
Pubmed ID
Book ISBNs
978-1-4939-8638-5, 978-1-4939-8639-2
Authors

Daniel Reker, J. B. Brown

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 23%
Unspecified 1 8%
Student > Doctoral Student 1 8%
Lecturer > Senior Lecturer 1 8%
Student > Ph. D. Student 1 8%
Other 3 23%
Unknown 3 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 31%
Biochemistry, Genetics and Molecular Biology 2 15%
Unspecified 1 8%
Computer Science 1 8%
Social Sciences 1 8%
Other 1 8%
Unknown 3 23%