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Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

Overview of attention for article published in BMC Genomics, April 2010
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
67 Dimensions

Readers on

mendeley
78 Mendeley
citeulike
4 CiteULike
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Title
Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes
Published in
BMC Genomics, April 2010
DOI 10.1186/1471-2164-11-222
Pubmed ID
Authors

Maria A Doyle, Robin B Gasser, Ben J Woodcroft, Ross S Hall, Stuart A Ralph

Abstract

New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Saccharomyces cerevisiae, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset.

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 1%
Spain 1 1%
Poland 1 1%
Kenya 1 1%
Unknown 74 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 27%
Student > Master 16 21%
Researcher 15 19%
Professor > Associate Professor 5 6%
Student > Bachelor 4 5%
Other 11 14%
Unknown 6 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 45%
Biochemistry, Genetics and Molecular Biology 15 19%
Computer Science 5 6%
Veterinary Science and Veterinary Medicine 2 3%
Engineering 2 3%
Other 7 9%
Unknown 12 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 24 January 2011.
All research outputs
#817,709
of 3,627,324 outputs
Outputs from BMC Genomics
#859
of 3,347 outputs
Outputs of similar age
#25,130
of 97,116 outputs
Outputs of similar age from BMC Genomics
#52
of 200 outputs
Altmetric has tracked 3,627,324 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 3,347 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 67% 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 97,116 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 200 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.