You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis
|
|---|---|
| Published in |
BMC Systems Biology, September 2014
|
| DOI | 10.1186/s12918-014-0111-5 |
| Pubmed ID | |
| Authors |
Jesse CJ van Dam, Peter J Schaap, Vitor AP Martins dos Santos, María Suárez-Diez |
| Abstract |
Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each separate set has an intrinsic value that is diluted and partly lost when building a consensus network. Here we present a methodology to generate co-expression networks and, instead of a consensus network, we propose an integration framework where the different networks are kept and analysed with additional tools to efficiently combine the information extracted from each network. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 57 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 20% |
| Researcher | 11 | 19% |
| Student > Master | 8 | 14% |
| Student > Doctoral Student | 5 | 8% |
| Professor > Associate Professor | 5 | 8% |
| Other | 15 | 25% |
| Unknown | 3 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 32% |
| Biochemistry, Genetics and Molecular Biology | 13 | 22% |
| Computer Science | 5 | 8% |
| Unspecified | 4 | 7% |
| Medicine and Dentistry | 3 | 5% |
| Other | 8 | 14% |
| Unknown | 7 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 26 September 2015.
All research outputs
#3,662,553
of 22,765,347 outputs
Outputs from BMC Systems Biology
#103
of 1,142 outputs
Outputs of similar age
#41,239
of 252,277 outputs
Outputs of similar age from BMC Systems Biology
#4
of 30 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 90% 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 252,277 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.