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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Characterizing cancer subtypes as attractors of Hopfield networks
|
|---|---|
| Published in |
Bioinformatics, January 2014
|
| DOI | 10.1093/bioinformatics/btt773 |
| Pubmed ID | |
| Authors |
Stefan R. Maetschke, Mark A. Ragan |
| Abstract |
Cancer is a heterogeneous progressive disease caused by perturbations of the underlying gene regulatory network that can be described by dynamic models. These dynamics are commonly modeled as Boolean networks or as ordinary differential equations. Their inference from data is computationally challenging, and at least partial knowledge of the regulatory network and its kinetic parameters is usually required to construct predictive models. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 67% |
| India | 1 | 17% |
| Norway | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Portugal | 1 | 1% |
| Germany | 1 | 1% |
| Italy | 1 | 1% |
| France | 1 | 1% |
| Ukraine | 1 | 1% |
| United Kingdom | 1 | 1% |
| Unknown | 76 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 23 | 27% |
| Student > Ph. D. Student | 21 | 25% |
| Student > Bachelor | 9 | 11% |
| Professor > Associate Professor | 7 | 8% |
| Student > Master | 5 | 6% |
| Other | 8 | 10% |
| Unknown | 11 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 27% |
| Computer Science | 16 | 19% |
| Biochemistry, Genetics and Molecular Biology | 13 | 15% |
| Engineering | 7 | 8% |
| Physics and Astronomy | 6 | 7% |
| Other | 9 | 11% |
| Unknown | 10 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 15 May 2014.
All research outputs
#7,301,532
of 25,373,627 outputs
Outputs from Bioinformatics
#6,057
of 12,808 outputs
Outputs of similar age
#79,574
of 318,732 outputs
Outputs of similar age from Bioinformatics
#102
of 194 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 52% 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 318,732 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 74% of its contemporaries.
We're also able to compare this research output to 194 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.