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Bioinformatics and Drug Discovery

Overview of attention for book
Cover of 'Bioinformatics and Drug Discovery'

Table of Contents

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    Book Overview
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    Chapter 1 Miniaturized Checkerboard Assays to Measure Antibiotic Interactions
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    Chapter 2 High-Throughput Screening for Drug Combinations
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    Chapter 3 Post-processing of Large Bioactivity Data
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    Chapter 4 How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM)
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    Chapter 5 A Guide to Dictionary-Based Text Mining
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    Chapter 6 Leveraging Big Data to Transform Drug Discovery
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    Chapter 7 How to Prepare a Compound Collection Prior to Virtual Screening
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    Chapter 8 Building a Quantitative Structure-Property Relationship (QSPR) Model
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    Chapter 9 Isomeric and Conformational Analysis of Small Drug and Drug-Like Molecules by Ion Mobility-Mass Spectrometry (IM-MS)
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    Chapter 10 A Computational Platform and Guide for Acceleration of Novel Medicines and Personalized Medicine
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    Chapter 11 Omics Data Integration and Analysis for Systems Pharmacology
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    Chapter 12 Bioinformatics-Based Tools and Software in Clinical Research: A New Emerging Area
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    Chapter 13 Text Mining for Drug Discovery
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    Chapter 14 Big Data Cohort Extraction for Personalized Statin Treatment and Machine Learning
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    Chapter 15 Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data
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    Chapter 16 Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data
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    Chapter 17 A Bayesian Network Approach to Disease Subtype Discovery
Attention for Chapter 6: Leveraging Big Data to Transform Drug Discovery
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Chapter title
Leveraging Big Data to Transform Drug Discovery
Chapter number 6
Book title
Bioinformatics and Drug Discovery
Published by
Humana Press, New York, NY, March 2019
DOI 10.1007/978-1-4939-9089-4_6
Pubmed ID
30848458
Book ISBNs
978-1-4939-9088-7, 978-1-4939-9089-4
Authors

Benjamin S. Glicksberg, Li Li, Rong Chen, Joel Dudley, Bin Chen, Glicksberg, Benjamin S., Li, Li, Chen, Rong, Dudley, Joel, Chen, Bin

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X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 22%
Student > Bachelor 8 14%
Student > Doctoral Student 4 7%
Student > Ph. D. Student 4 7%
Student > Master 4 7%
Other 5 9%
Unknown 20 34%
Readers by discipline Count As %
Medicine and Dentistry 7 12%
Biochemistry, Genetics and Molecular Biology 6 10%
Agricultural and Biological Sciences 6 10%
Computer Science 4 7%
Pharmacology, Toxicology and Pharmaceutical Science 4 7%
Other 8 14%
Unknown 23 40%

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