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Social networks help to infer causality in the tumor microenvironment

Overview of attention for article published in BMC Research Notes, March 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
18 Mendeley
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Title
Social networks help to infer causality in the tumor microenvironment
Published in
BMC Research Notes, March 2016
DOI 10.1186/s13104-016-1976-8
Pubmed ID
Authors

Isaac Crespo, Marie-Agnès Doucey, Ioannis Xenarios

Abstract

Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems-such as the tumor microenvironment-where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies. In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature. This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Other 3 17%
Researcher 2 11%
Student > Bachelor 1 6%
Student > Doctoral Student 1 6%
Student > Ph. D. Student 1 6%
Other 3 17%
Unknown 7 39%
Readers by discipline Count As %
Social Sciences 2 11%
Biochemistry, Genetics and Molecular Biology 2 11%
Agricultural and Biological Sciences 1 6%
Nursing and Health Professions 1 6%
Unspecified 1 6%
Other 4 22%
Unknown 7 39%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 April 2016.
All research outputs
#3,261,131
of 7,551,260 outputs
Outputs from BMC Research Notes
#712
of 1,895 outputs
Outputs of similar age
#113,231
of 269,823 outputs
Outputs of similar age from BMC Research Notes
#36
of 93 outputs
Altmetric has tracked 7,551,260 research outputs across all sources so far. This one has received more attention than most of these and is in the 55th percentile.
So far Altmetric has tracked 1,895 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 60% 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 269,823 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 56% of its contemporaries.
We're also able to compare this research output to 93 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 59% of its contemporaries.