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Signal reachability facilitates characterization of probabilistic signaling networks

Overview of attention for article published in BMC Bioinformatics, December 2015
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Title
Signal reachability facilitates characterization of probabilistic signaling networks
Published in
BMC Bioinformatics, December 2015
DOI 10.1186/1471-2105-16-s17-s6
Pubmed ID
Authors

Haitham Gabr, Tamer Kahveci

Abstract

Studying biological networks is of extreme importance in understanding cellular functions. These networks model interactions between molecules in each cell. A large volume of research has been done to uncover different characteristics of biological networks, such as large-scale organization, node centrality and network robustness. Nevertheless, the vast majority of research done in this area assume that biological networks have deterministic topologies. Biological interactions are however probabilistic events that may or may not appear at different cells or even in the same cell at different times. In this paper, we present novel methods for characterizing probabilistic signaling networks. Our methods do this by computing the probability that a signal propagates successfully from receptor to reporter genes through interactions in the network. We characterize such networks with respect to (i) centrality of individual nodes, (ii) stability of the entire network, and (iii) important functions served by the network. We use these methods to characterize major H. sapiens signaling networks including Wnt, ErbB and MAPK.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 25%
Student > Ph. D. Student 2 17%
Professor 1 8%
Student > Master 1 8%
Unknown 5 42%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 17%
Computer Science 2 17%
Agricultural and Biological Sciences 1 8%
Chemistry 1 8%
Unknown 6 50%
Attention Score in Context

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 08 December 2015.
All research outputs
#18,431,664
of 22,834,308 outputs
Outputs from BMC Bioinformatics
#6,320
of 7,288 outputs
Outputs of similar age
#280,337
of 388,302 outputs
Outputs of similar age from BMC Bioinformatics
#139
of 159 outputs
Altmetric has tracked 22,834,308 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,288 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 388,302 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.