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Computational Methods for Drug Repurposing

Overview of attention for book
Cover of 'Computational Methods for Drug Repurposing'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing
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    Chapter 2 Performing an In Silico Repurposing of Existing Drugs by Combining Virtual Screening and Molecular Dynamics Simulation
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    Chapter 3 Repurposing Drugs Based on Evolutionary Relationships Between Targets of Approved Drugs and Proteins of Interest
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    Chapter 4 Drug Repositioning by Mining Adverse Event Data in ClinicalTrials.gov
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    Chapter 5 Transcriptomic Data Mining and Repurposing for Computational Drug Discovery
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    Chapter 6 Network-Based Drug Repositioning: Approaches, Resources, and Research Directions
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    Chapter 7 A Computational Bipartite Graph-Based Drug Repurposing Method
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    Chapter 8 Implementation of a Pipeline Using Disease-Disease Associations for Computational Drug Repurposing
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    Chapter 9 An Application of Computational Drug Repurposing Based on Transcriptomic Signatures
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    Chapter 10 Drug-Induced Expression-Based Computational Repurposing of Small Molecules Affecting Transcription Factor Activity
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    Chapter 11 A Drug Repurposing Method Based on Drug-Drug Interaction Networks and Using Energy Model Layouts
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    Chapter 12 Integrating Biological Networks for Drug Target Prediction and Prioritization
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    Chapter 13 Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing
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    Chapter 14 Computational Prediction of Drug-Target Interactions via Ensemble Learning
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    Chapter 15 A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization
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    Chapter 16 Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities
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    Chapter 17 A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources
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    Chapter 18 Heter-LP: A Heterogeneous Label Propagation Method for Drug Repositioning
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    Chapter 19 Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction
Attention for Chapter 6: Network-Based Drug Repositioning: Approaches, Resources, and Research Directions
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Chapter title
Network-Based Drug Repositioning: Approaches, Resources, and Research Directions
Chapter number 6
Book title
Computational Methods for Drug Repurposing
Published in
Methods in molecular biology, December 2018
DOI 10.1007/978-1-4939-8955-3_6
Pubmed ID
30547438
Book ISBNs
978-1-4939-8954-6, 978-1-4939-8955-3
Authors

Salvatore Alaimo, Alfredo Pulvirenti, Alaimo, Salvatore, Pulvirenti, Alfredo

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 19%
Student > Master 6 14%
Researcher 6 14%
Student > Bachelor 5 12%
Professor 1 2%
Other 2 5%
Unknown 15 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 14%
Computer Science 6 14%
Chemistry 4 9%
Medicine and Dentistry 4 9%
Pharmacology, Toxicology and Pharmaceutical Science 3 7%
Other 6 14%
Unknown 14 33%
Attention Score in Context

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 18 December 2018.
All research outputs
#15,708,425
of 23,344,526 outputs
Outputs from Methods in molecular biology
#5,497
of 13,338 outputs
Outputs of similar age
#266,088
of 438,229 outputs
Outputs of similar age from Methods in molecular biology
#31
of 46 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,338 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 438,229 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.

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