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Mendeley readers
Attention Score in Context
| Title |
PathwayMatrix: visualizing binary relationships between proteins in biological pathways
|
|---|---|
| Published in |
BMC Proceedings, August 2015
|
| DOI | 10.1186/1753-6561-9-s6-s3 |
| Pubmed ID | |
| Authors |
Tuan Nhon Dang, Paul Murray, Angus Graeme Forbes |
| Abstract |
Molecular activation pathways are inherently complex, and understanding relations across many biochemical reactions and reaction types is difficult. Visualizing and analyzing a pathway is a challenge due to the network size and the diversity of relations between proteins and molecules. In this paper, we introduce PathwayMatrix, a visualization tool that presents the binary relations between proteins in the pathway via the use of an interactive adjacency matrix. We provide filtering, lensing, clustering, and brushing and linking capabilities in order to present relevant details about proteins within a pathway. We evaluated PathwayMatrix by conducting a series of in-depth interviews with domain experts who provided positive feedback, leading us to believe that our visualization technique could be helpful for the larger community of researchers utilizing pathway visualizations. PathwayMatrix is freely available at https://github.com/CreativeCodingLab/PathwayMatrix. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 8% |
| Luxembourg | 1 | 8% |
| Belgium | 1 | 8% |
| Unknown | 10 | 77% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 23% |
| Student > Postgraduate | 2 | 15% |
| Student > Master | 2 | 15% |
| Professor > Associate Professor | 2 | 15% |
| Other | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 2 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 23% |
| Medicine and Dentistry | 2 | 15% |
| Agricultural and Biological Sciences | 2 | 15% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Sports and Recreations | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 3 | 23% |
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 12 September 2015.
All research outputs
#19,701,336
of 24,226,848 outputs
Outputs from BMC Proceedings
#285
of 394 outputs
Outputs of similar age
#195,900
of 268,884 outputs
Outputs of similar age from BMC Proceedings
#6
of 6 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 394 research outputs from this source. They receive a mean Attention Score of 4.1. 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 268,884 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.