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Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network

Overview of attention for article published in BMC Systems Biology, December 2012
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1 tweeter

Citations

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17 Dimensions

Readers on

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34 Mendeley
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Title
Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network
Published in
BMC Systems Biology, December 2012
DOI 10.1186/1752-0509-6-s3-s3
Pubmed ID
Authors

Tingting Qin, Lam C Tsoi, Kellie J Sims, Xinghua Lu, W Jim Zheng

Abstract

Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 33 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 26%
Researcher 5 15%
Student > Master 5 15%
Professor > Associate Professor 4 12%
Student > Postgraduate 3 9%
Other 6 18%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 35%
Computer Science 9 26%
Biochemistry, Genetics and Molecular Biology 5 15%
Medicine and Dentistry 4 12%
Engineering 2 6%
Other 0 0%
Unknown 2 6%

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 25 January 2013.
All research outputs
#15,055,021
of 18,796,975 outputs
Outputs from BMC Systems Biology
#816
of 1,127 outputs
Outputs of similar age
#194,318
of 263,305 outputs
Outputs of similar age from BMC Systems Biology
#30
of 46 outputs
Altmetric has tracked 18,796,975 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 1,127 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 12th percentile – i.e., 12% 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 263,305 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.