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X Demographics
Mendeley readers
Attention Score in Context
| Title |
CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks
|
|---|---|
| Published in |
BMC Bioinformatics, February 2016
|
| DOI | 10.1186/s12859-016-0914-z |
| Pubmed ID | |
| Authors |
Andrea Paroni, Alex Graudenzi, Giulio Caravagna, Chiara Damiani, Giancarlo Mauri, Marco Antoniotti |
| Abstract |
Dynamical models of gene regulatory networks (GRNs) are highly effective in describing complex biological phenomena and processes, such as cell differentiation and cancer development. Yet, the topological and functional characterization of real GRNs is often still partial and an exhaustive picture of their functioning is missing. We here introduce CABERNET, a Cytoscape app for the generation, simulation and analysis of Boolean models of GRNs, specifically focused on their augmentation when a only partial topological and functional characterization of the network is available. By generating large ensembles of networks in which user-defined entities and relations are added to the original core, CABERNET allows to formulate hypotheses on the missing portions of real networks, as well to investigate their generic properties, in the spirit of complexity science. CABERNET offers a series of innovative simulation and modeling functions and tools, including (but not being limited to) the dynamical characterization of the gene activation patterns ruling cell types and differentiation fates, and sophisticated robustness assessments, as in the case of gene knockouts. The integration within the widely used Cytoscape framework for the visualization and analysis of biological networks, makes CABERNET a new essential instrument for both the bioinformatician and the computational biologist, as well as a computational support for the experimentalist. An example application concerning the analysis of an augmented T-helper cell GRN is provided. |
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 % |
|---|---|---|
| Italy | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 55 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 22% |
| Researcher | 12 | 22% |
| Student > Master | 7 | 13% |
| Student > Bachelor | 6 | 11% |
| Professor > Associate Professor | 4 | 7% |
| Other | 8 | 15% |
| Unknown | 6 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 27% |
| Biochemistry, Genetics and Molecular Biology | 13 | 24% |
| Computer Science | 8 | 15% |
| Mathematics | 4 | 7% |
| Medicine and Dentistry | 3 | 5% |
| Other | 6 | 11% |
| Unknown | 6 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 08 February 2016.
All research outputs
#13,964,379
of 22,842,950 outputs
Outputs from BMC Bioinformatics
#4,478
of 7,289 outputs
Outputs of similar age
#201,630
of 397,006 outputs
Outputs of similar age from BMC Bioinformatics
#86
of 134 outputs
Altmetric has tracked 22,842,950 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,289 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 35th percentile – i.e., 35% 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 397,006 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.