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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DiNAR: revealing hidden patterns of plant signalling dynamics using Differential Network Analysis in R
|
|---|---|
| Published in |
Plant Methods, August 2018
|
| DOI | 10.1186/s13007-018-0345-0 |
| Pubmed ID | |
| Authors |
Maja Zagorščak, Andrej Blejec, Živa Ramšak, Marko Petek, Tjaša Stare, Kristina Gruden |
| Abstract |
Progress in high-throughput molecular methods accompanied by more complex experimental designs demands novel data visualisation solutions. To specifically answer the question which parts of the specifical biological system are responding in particular perturbation, integrative approach in which experimental data are superimposed on a prior knowledge network is shown to be advantageous. We have developed DiNAR, Differential Network Analysis in R, a user-friendly application with dynamic visualisation that integrates multiple condition high-throughput data and extensive biological prior knowledge. Implemented differential network approach and embedded network analysis allow users to analyse condition-specific responses in the context of topology of interest (e.g. immune signalling network) and extract knowledge concerning patterns of signalling dynamics (i.e. rewiring in network structure between two or more biological conditions). We validated the usability of software on the Arabidopsis thaliana and Solanum tuberosum datasets, but it is set to handle any biological instances. DiNAR facilitates detection of network-rewiring events, gene prioritisation for future experimental design and allows capturing dynamics of complex biological system. The fully cross-platform Shiny App is hosted and freely available at https://nib-si.shinyapps.io/DiNAR. The most recent version of the source code is available at https://github.com/NIB-SI/DiNAR/ with a DOI 10.5281/zenodo.1230523 of the archived version in Zenodo. |
X Demographics
The data shown below were collected from the profiles of 24 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Slovenia | 3 | 13% |
| United Kingdom | 3 | 13% |
| United States | 3 | 13% |
| Spain | 2 | 8% |
| Cameroon | 1 | 4% |
| Brazil | 1 | 4% |
| India | 1 | 4% |
| Greece | 1 | 4% |
| Germany | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 6 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 46% |
| Scientists | 10 | 42% |
| Science communicators (journalists, bloggers, editors) | 2 | 8% |
| Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 48 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 31% |
| Student > Ph. D. Student | 12 | 25% |
| Student > Master | 6 | 13% |
| Student > Bachelor | 4 | 8% |
| Professor > Associate Professor | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 7 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 42% |
| Biochemistry, Genetics and Molecular Biology | 5 | 10% |
| Computer Science | 3 | 6% |
| Mathematics | 2 | 4% |
| Immunology and Microbiology | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 11 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 12 January 2019.
All research outputs
#2,258,458
of 25,079,481 outputs
Outputs from Plant Methods
#100
of 1,229 outputs
Outputs of similar age
#45,134
of 340,077 outputs
Outputs of similar age from Plant Methods
#3
of 30 outputs
Altmetric has tracked 25,079,481 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,229 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done particularly well, scoring higher than 91% 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 340,077 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 86% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.