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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 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 | 66 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 24% |
| Researcher | 17 | 23% |
| Student > Master | 8 | 11% |
| Professor > Associate Professor | 6 | 8% |
| Student > Postgraduate | 5 | 7% |
| Other | 16 | 22% |
| Unknown | 4 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 24 | 32% |
| Agricultural and Biological Sciences | 18 | 24% |
| Biochemistry, Genetics and Molecular Biology | 10 | 14% |
| Immunology and Microbiology | 5 | 7% |
| Engineering | 4 | 5% |
| Other | 8 | 11% |
| Unknown | 5 | 7% |
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
#17,734,890
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#5,927
of 7,276 outputs
Outputs of similar age
#247,586
of 361,188 outputs
Outputs of similar age from BMC Bioinformatics
#111
of 135 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 361,188 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.