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Computing interaction probabilities in signaling networks

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, November 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
4 tweeters

Citations

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4 Dimensions

Readers on

mendeley
7 Mendeley
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Title
Computing interaction probabilities in signaling networks
Published in
EURASIP Journal on Bioinformatics & Systems Biology, November 2015
DOI 10.1186/s13637-015-0031-8
Pubmed ID
Authors

Haitham Gabr, Juan Carlos Rivera-Mulia, David M. Gilbert, Tamer Kahveci

Abstract

Biological networks inherently have uncertain topologies. This arises from many factors. For instance, interactions between molecules may or may not take place under varying conditions. Genetic or epigenetic mutations may also alter biological processes like transcription or translation. This uncertainty is often modeled by associating each interaction with a probability value. Studying biological networks under this probabilistic model has already been shown to yield accurate and insightful analysis of interaction data. However, the problem of assigning accurate probability values to interactions remains unresolved. In this paper, we present a novel method for computing interaction probabilities in signaling networks based on transcription levels of genes. The transcription levels define the signal reachability probability between membrane receptors and transcription factors. Our method computes the interaction probabilities that minimize the gap between the observed and the computed signal reachability probabilities. We evaluate our method on four signaling networks from the Kyoto Encyclopedia of Genes and Genomes (KEGG). For each network, we compute its edge probabilities using the gene expression profiles for seven major leukemia subtypes. We use these values to analyze how the stress induced by different leukemia subtypes affects signaling interactions.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 14%
Unknown 6 86%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 3 43%
Student > Ph. D. Student 2 29%
Student > Bachelor 1 14%
Professor 1 14%
Readers by discipline Count As %
Medicine and Dentistry 2 29%
Computer Science 2 29%
Physics and Astronomy 1 14%
Biochemistry, Genetics and Molecular Biology 1 14%
Unknown 1 14%

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 11 December 2015.
All research outputs
#7,032,376
of 12,457,990 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#14
of 51 outputs
Outputs of similar age
#139,526
of 337,963 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#2
of 6 outputs
Altmetric has tracked 12,457,990 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 51 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 70% 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 337,963 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 57% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.