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Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Unknown | 38 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 23% |
| Researcher | 5 | 13% |
| Student > Master | 5 | 13% |
| Professor > Associate Professor | 4 | 10% |
| Student > Bachelor | 4 | 10% |
| Other | 8 | 21% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 33% |
| Computer Science | 10 | 26% |
| Medicine and Dentistry | 6 | 15% |
| Biochemistry, Genetics and Molecular Biology | 4 | 10% |
| Engineering | 2 | 5% |
| Other | 0 | 0% |
| Unknown | 4 | 10% |
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
#18,326,065
of 22,693,205 outputs
Outputs from BMC Systems Biology
#834
of 1,142 outputs
Outputs of similar age
#203,088
of 261,299 outputs
Outputs of similar age from BMC Systems Biology
#41
of 56 outputs
Altmetric has tracked 22,693,205 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 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% 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 261,299 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.