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 |
PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways
|
|---|---|
| Published in |
BMC Bioinformatics, May 2015
|
| DOI | 10.1186/s12859-015-0585-1 |
| Pubmed ID | |
| Authors |
Yongnan Zhu, Liang Sun, Alexander Garbarino, Carl Schmidt, Jinglong Fang, Jian Chen |
| Abstract |
High-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists. We present PathRings, a new interactive web-based, scalable biological pathway visualization tool for biologists to explore and interpret biological pathways. PathRings integrates metabolic and signaling pathways from Reactome in a single compound graph visualization, and uses color to highlight genes and pathways affected by input data. Pathways are available for multiple species and analysis of user-defined species or input is also possible. PathRings permits an overview of the impact of gene expression data on all pathways to facilitate visual pattern finding. Detailed pathways information can be opened in new visualizations while maintaining the overview, that form a visual exploration provenance. A dynamic multi-view bubbles interface is designed to support biologists' analytical tasks by letting users construct incremental views that further reflect biologists' analytical process. This approach decomposes complex tasks into simpler ones and automates multi-view management. PathRings has been designed to accommodate interactive visual analysis of experimental data in the context of pathways defined by Reactome. Our new approach to interface design can effectively support comparative tasks over substantially larger collection than existing tools. The dynamic interaction among multi-view dataset visualization improves the data exploration. PathRings is available free at http://raven.anr.udel.edu/~sunliang/PathRings and the source code is hosted on Github: https://github.com/ivcl/PathRings . |
X Demographics
The data shown below were collected from the profiles of 7 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 | 2 | 29% |
| Spain | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 57% |
| Members of the public | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 2% |
| Singapore | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 42 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 31% |
| Student > Ph. D. Student | 12 | 27% |
| Student > Bachelor | 7 | 16% |
| Professor > Associate Professor | 3 | 7% |
| Other | 2 | 4% |
| Other | 5 | 11% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 36% |
| Computer Science | 11 | 24% |
| Engineering | 5 | 11% |
| Biochemistry, Genetics and Molecular Biology | 4 | 9% |
| Psychology | 2 | 4% |
| Other | 3 | 7% |
| Unknown | 4 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 01 October 2015.
All research outputs
#7,229,557
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#2,740
of 7,418 outputs
Outputs of similar age
#83,795
of 267,766 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 119 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 61% 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 267,766 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 119 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.