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Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Overview of attention for article published in BMC Bioinformatics, December 2014
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Mentioned by

twitter
2 tweeters

Citations

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16 Dimensions

Readers on

mendeley
70 Mendeley
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1 CiteULike
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Title
Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0387-x
Pubmed ID
Authors

Enzo Acerbi, Teresa Zelante, Vipin Narang, Fabio Stella

Abstract

Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models' expressiveness.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 4%
Germany 1 1%
Netherlands 1 1%
Finland 1 1%
Russia 1 1%
Japan 1 1%
Unknown 62 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 24%
Researcher 17 24%
Student > Master 8 11%
Professor > Associate Professor 6 9%
Student > Postgraduate 5 7%
Other 15 21%
Unknown 2 3%
Readers by discipline Count As %
Computer Science 23 33%
Agricultural and Biological Sciences 18 26%
Biochemistry, Genetics and Molecular Biology 10 14%
Immunology and Microbiology 5 7%
Engineering 3 4%
Other 7 10%
Unknown 4 6%

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 11 December 2014.
All research outputs
#11,069,903
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#4,222
of 5,420 outputs
Outputs of similar age
#186,843
of 301,382 outputs
Outputs of similar age from BMC Bioinformatics
#258
of 321 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% 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 301,382 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 321 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.