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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
  2. Altmetric Badge
    Chapter 1 Cell Perturbation Screens for Target Identification by RNAi
  3. Altmetric Badge
    Chapter 2 Using Functional Genomics to Identify Drug Targets: A Dupuytren’s Disease Example
  4. Altmetric Badge
    Chapter 3 Functional Characterization of Human Genes from Exon Expression and RNA Interference Results
  5. Altmetric Badge
    Chapter 4 Barcode sequencing for understanding drug-gene interactions.
  6. Altmetric Badge
    Chapter 5 High-Throughput Sequencing of the Methylome Using Two-Base Encoding
  7. Altmetric Badge
    Chapter 6 Bioinformatics and Drug Discovery
  8. Altmetric Badge
    Chapter 7 Compound Collection Preparation for Virtual Screening
  9. Altmetric Badge
    Chapter 8 Mapping between databases of compounds and protein targets.
  10. Altmetric Badge
    Chapter 9 Predictive Cheminformatics in Drug Discovery: Statistical Modeling for Analysis of Micro-array and Gene Expression Data
  11. Altmetric Badge
    Chapter 10 Advances in Nuclear Magnetic Resonance for Drug Discovery
  12. Altmetric Badge
    Chapter 11 Human ABC Transporter ABCG2 in Cancer Chemotherapy: Drug Molecular Design to Circumvent Multidrug Resistance
  13. Altmetric Badge
    Chapter 12 Protein Interactions: Mapping Interactome Networks to Support Drug Target Discovery and Selection
  14. Altmetric Badge
    Chapter 13 Linking Variants from Genome-Wide Association Analysis to Function via Transcriptional Network Analysis
  15. Altmetric Badge
    Chapter 14 Models of Excitation–Contraction Coupling in Cardiac Ventricular Myocytes
  16. Altmetric Badge
    Chapter 15 Integration of Multiple Ubiquitin Signals in Proteasome Regulation
Attention for Chapter 8: Mapping between databases of compounds and protein targets.
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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5 X users
peer_reviews
1 peer review site
wikipedia
1 Wikipedia page

Citations

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4 Dimensions

Readers on

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31 Mendeley
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1 CiteULike
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Chapter title
Mapping between databases of compounds and protein targets.
Chapter number 8
Book title
Bioinformatics and Drug Discovery
Published in
Methods in molecular biology, June 2012
DOI 10.1007/978-1-61779-965-5_8
Pubmed ID
Book ISBNs
978-1-61779-964-8, 978-1-61779-965-5
Authors

Muresan S, Sitzmann M, Southan C, Muresan, Sorel, Sitzmann, Markus, Southan, Christopher, Sorel Muresan, Markus Sitzmann, Christopher Southan

Abstract

Databases that provide links between bioactive compounds and their protein targets are increasingly important in drug discovery and chemical biology. They join the expanding universes of cheminformatics via chemical structures on the one hand and bioinformatics via sequences on the other. However, it is difficult to assess the relative utility of databases without the explicit comparison of content. We have exemplified an approach to this by comparing resources that each has a different focus on bioactive chemistry (ChEMBL, DrugBank, Human Metabolome Database, and Therapeutic Target Database) both at the chemical structure and protein levels. We compared the compound sets at different representational stringencies using NCI/CADD Structure Identifiers. The overlap and uniqueness in chemical content can be broadly interpreted in the context of different data capture strategies. However, we recorded apparent anomalies, such as many compounds-in-common between the metabolite and drug databases. We also compared the content of sequences mapped to the compounds via their UniProt protein identifiers. While these were also generally interpretable in the context of individual databases we discerned differences in coverage and the types of supporting data used. For example, the target concept is applied differently between DrugBank and the Therapeutic Target Database. In ChEMBL it encompasses a broader range of mappings from chemical biology and species orthologue cross-screening in addition to drug targets per se. Our analysis should assist users not only in exploiting the synergies between these four high-value resources but also in assessing the utility of other databases at the interface of chemistry and biology.

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X Demographics

The data shown below were collected from the profiles of 5 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
Netherlands 1 3%
United States 1 3%
Sweden 1 3%
Unknown 27 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 35%
Student > Ph. D. Student 7 23%
Student > Master 6 19%
Lecturer 2 6%
Unspecified 1 3%
Other 2 6%
Unknown 2 6%
Readers by discipline Count As %
Chemistry 10 32%
Agricultural and Biological Sciences 7 23%
Biochemistry, Genetics and Molecular Biology 4 13%
Computer Science 4 13%
Engineering 2 6%
Other 2 6%
Unknown 2 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 27 November 2023.
All research outputs
#4,976,877
of 24,878,531 outputs
Outputs from Methods in molecular biology
#1,416
of 13,978 outputs
Outputs of similar age
#31,920
of 169,492 outputs
Outputs of similar age from Methods in molecular biology
#7
of 46 outputs
Altmetric has tracked 24,878,531 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,978 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 89% of its peers.
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 169,492 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
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 has done well, scoring higher than 86% of its contemporaries.