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Mendeley readers
Attention Score in Context
| 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 4% |
| Luxembourg | 1 | 2% |
| Unknown | 46 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 27% |
| Researcher | 12 | 24% |
| Student > Postgraduate | 4 | 8% |
| Student > Master | 4 | 8% |
| Student > Bachelor | 3 | 6% |
| Other | 6 | 12% |
| Unknown | 7 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 33% |
| Biochemistry, Genetics and Molecular Biology | 7 | 14% |
| Immunology and Microbiology | 6 | 12% |
| Computer Science | 4 | 8% |
| Medicine and Dentistry | 3 | 6% |
| Other | 4 | 8% |
| Unknown | 9 | 18% |
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
#7,454,298
of 22,789,076 outputs
Outputs from BMC Genomics
#3,597
of 10,647 outputs
Outputs of similar age
#59,111
of 183,796 outputs
Outputs of similar age from BMC Genomics
#53
of 142 outputs
Altmetric has tracked 22,789,076 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,647 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 59% 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 183,796 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 142 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 57% of its contemporaries.