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An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation

Overview of attention for article published in BMC Genomics, October 2012
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

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22 Dimensions

Readers on

mendeley
44 Mendeley
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Title
An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation
Published in
BMC Genomics, October 2012
DOI 10.1186/1471-2164-13-572
Pubmed ID
Authors

Tarmo Äijö, Sanna M Edelman, Tapio Lönnberg, Antti Larjo, Henna Kallionpää, Soile Tuomela, Emilia Engström, Riitta Lahesmaa, Harri Lähdesmäki

Abstract

A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior.

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 5%
Luxembourg 1 2%
Unknown 41 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 30%
Researcher 11 25%
Student > Postgraduate 4 9%
Student > Master 4 9%
Student > Bachelor 3 7%
Other 6 14%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 34%
Biochemistry, Genetics and Molecular Biology 6 14%
Immunology and Microbiology 6 14%
Medicine and Dentistry 4 9%
Computer Science 4 9%
Other 5 11%
Unknown 4 9%

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 16 January 2013.
All research outputs
#817,201
of 3,628,259 outputs
Outputs from BMC Genomics
#859
of 3,347 outputs
Outputs of similar age
#24,701
of 95,153 outputs
Outputs of similar age from BMC Genomics
#53
of 202 outputs
Altmetric has tracked 3,628,259 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 95,153 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 72% of its contemporaries.
We're also able to compare this research output to 202 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.