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Integrating the interactome and the transcriptome of Drosophila

Overview of attention for article published in BMC Bioinformatics, June 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Title
Integrating the interactome and the transcriptome of Drosophila
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-177
Pubmed ID
Authors

Thilakam Murali, Svetlana Pacifico, Russell L Finley

Abstract

Networks of interacting genes and gene products mediate most cellular and developmental processes. High throughput screening methods combined with literature curation are identifying many of the protein-protein interactions (PPI) and protein-DNA interactions (PDI) that constitute these networks. Most of the detection methods, however, fail to identify the in vivo spatial or temporal context of the interactions. Thus, the interaction data are a composite of the individual networks that may operate in specific tissues or developmental stages. Genome-wide expression data may be useful for filtering interaction data to identify the subnetworks that operate in specific spatial or temporal contexts. Here we take advantage of the extensive interaction and expression data available for Drosophila to analyze how interaction networks may be unique to specific tissues and developmental stages.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 2%
Korea, Republic of 1 2%
Unknown 47 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 35%
Researcher 12 24%
Student > Master 8 16%
Student > Postgraduate 3 6%
Professor 2 4%
Other 4 8%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 59%
Biochemistry, Genetics and Molecular Biology 7 14%
Computer Science 6 12%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Medicine and Dentistry 1 2%
Other 2 4%
Unknown 3 6%
Attention Score in Context

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 12 June 2014.
All research outputs
#13,243,031
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#3,862
of 7,387 outputs
Outputs of similar age
#107,641
of 230,591 outputs
Outputs of similar age from BMC Bioinformatics
#68
of 158 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 45th percentile – i.e., 45% 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 230,591 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 52% of its contemporaries.
We're also able to compare this research output to 158 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 53% of its contemporaries.