You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
The extended TILAR approach: a novel tool for dynamic modeling of the transcription factor network regulating the adaption to in vitro cultivation of murine hepatocytes
|
|---|---|
| Published in |
BMC Systems Biology, November 2012
|
| DOI | 10.1186/1752-0509-6-147 |
| Pubmed ID | |
| Authors |
Sebastian Vlaic, Wolfgang Schmidt-Heck, Madlen Matz-Soja, Eugenia Marbach, Jörg Linde, Anke Meyer-Baese, Sebastian Zellmer, Reinhard Guthke, Rolf Gebhardt |
| Abstract |
Network inference is an important tool to reveal the underlying interactions of biological systems. In the liver, a complex system of transcription factors is active to distribute signals and induce the cellular response following extracellular stimuli. Plenty of information is available about single transcription factors important for the different functions of the liver, but little is known about their causal relations to each other. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 75% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Taiwan | 1 | 3% |
| Unknown | 28 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 28% |
| Student > Ph. D. Student | 7 | 24% |
| Student > Bachelor | 2 | 7% |
| Professor > Associate Professor | 2 | 7% |
| Student > Doctoral Student | 1 | 3% |
| Other | 4 | 14% |
| Unknown | 5 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 24% |
| Computer Science | 5 | 17% |
| Biochemistry, Genetics and Molecular Biology | 4 | 14% |
| Engineering | 3 | 10% |
| Mathematics | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 4 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 19 May 2020.
All research outputs
#6,754,462
of 25,374,917 outputs
Outputs from BMC Systems Biology
#203
of 1,132 outputs
Outputs of similar age
#64,546
of 285,954 outputs
Outputs of similar age from BMC Systems Biology
#7
of 35 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 81% 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 285,954 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.