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In silico modeling for tumor growth visualization

Overview of attention for article published in BMC Systems Biology, August 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
In silico modeling for tumor growth visualization
Published in
BMC Systems Biology, August 2016
DOI 10.1186/s12918-016-0318-8
Pubmed ID
Authors

Fleur Jeanquartier, Claire Jean-Quartier, David Cemernek, Andreas Holzinger

Abstract

Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research. We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ . In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
France 1 1%
Austria 1 1%
Unknown 68 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 19%
Researcher 13 18%
Student > Bachelor 11 15%
Student > Master 8 11%
Professor > Associate Professor 6 8%
Other 8 11%
Unknown 12 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 14%
Computer Science 9 13%
Engineering 6 8%
Medicine and Dentistry 6 8%
Mathematics 4 6%
Other 19 26%
Unknown 18 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 16 September 2016.
All research outputs
#5,661,090
of 23,577,761 outputs
Outputs from BMC Systems Biology
#174
of 1,143 outputs
Outputs of similar age
#94,284
of 366,776 outputs
Outputs of similar age from BMC Systems Biology
#8
of 32 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,143 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 84% 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 366,776 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 74% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.