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X Demographics
Mendeley readers
Attention Score in Context
| Title |
TimeXNet: Identifying active gene sub-networks using time-course gene expression profiles
|
|---|---|
| Published in |
BMC Systems Biology, December 2014
|
| DOI | 10.1186/1752-0509-8-s4-s2 |
| Pubmed ID | |
| Authors |
Ashwini Patil, Kenta Nakai |
| Abstract |
Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. When combined with large-scale molecular interaction networks, such data can provide information about the dynamics of cellular response to stimulus. However, few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles. |
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 % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 24 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 29% |
| Student > Ph. D. Student | 6 | 25% |
| Professor | 4 | 17% |
| Student > Master | 3 | 13% |
| Professor > Associate Professor | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 3 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 29% |
| Biochemistry, Genetics and Molecular Biology | 7 | 29% |
| Computer Science | 4 | 17% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Neuroscience | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 4 | 17% |
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 19 August 2015.
All research outputs
#14,206,722
of 22,774,233 outputs
Outputs from BMC Systems Biology
#544
of 1,142 outputs
Outputs of similar age
#191,460
of 360,807 outputs
Outputs of similar age from BMC Systems Biology
#25
of 53 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% 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 47th percentile – i.e., 47% 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 360,807 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.