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CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

Overview of attention for article published in BMC Bioinformatics, September 2017
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Title
CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components
Published in
BMC Bioinformatics, September 2017
DOI 10.1186/s12859-017-1787-5
Pubmed ID
Authors

Henry Heberle, Marcelo Falsarella Carazzolle, Guilherme P. Telles, Gabriela Vaz Meirelles, Rosane Minghim

Abstract

The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 22%
Researcher 12 22%
Student > Master 6 11%
Student > Doctoral Student 4 7%
Student > Bachelor 4 7%
Other 7 13%
Unknown 9 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 28%
Biochemistry, Genetics and Molecular Biology 9 17%
Computer Science 9 17%
Medicine and Dentistry 4 7%
Engineering 2 4%
Other 3 6%
Unknown 12 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 March 2021.
All research outputs
#14,117,254
of 24,137,933 outputs
Outputs from BMC Bioinformatics
#4,118
of 7,505 outputs
Outputs of similar age
#159,333
of 319,736 outputs
Outputs of similar age from BMC Bioinformatics
#48
of 101 outputs
Altmetric has tracked 24,137,933 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,505 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 42nd percentile – i.e., 42% 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 319,736 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.