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Mendeley readers
Attention Score in Context
| Title |
Pathway network inference from gene expression data
|
|---|---|
| Published in |
BMC Systems Biology, March 2014
|
| DOI | 10.1186/1752-0509-8-s2-s7 |
| Pubmed ID | |
| Authors |
Ignacio Ponzoni, María José Nueda, Sonia Tarazona, Stefan Götz, David Montaner, Julieta Sol Dussaut, Joaquín Dopazo, Ana Conesa |
| Abstract |
The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. |
X Demographics
The data shown below were collected from the profiles of 4 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 Kingdom | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Pakistan | 1 | <1% |
| Colombia | 1 | <1% |
| Spain | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 123 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 38 | 29% |
| Student > Ph. D. Student | 35 | 27% |
| Student > Master | 18 | 14% |
| Student > Bachelor | 11 | 9% |
| Student > Doctoral Student | 4 | 3% |
| Other | 17 | 13% |
| Unknown | 6 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 38 | 29% |
| Biochemistry, Genetics and Molecular Biology | 29 | 22% |
| Computer Science | 19 | 15% |
| Medicine and Dentistry | 8 | 6% |
| Mathematics | 4 | 3% |
| Other | 20 | 16% |
| Unknown | 11 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 16 February 2017.
All research outputs
#12,901,057
of 22,758,963 outputs
Outputs from BMC Systems Biology
#432
of 1,142 outputs
Outputs of similar age
#104,264
of 221,253 outputs
Outputs of similar age from BMC Systems Biology
#8
of 21 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% 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 has gotten more attention than average, scoring higher than 60% 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 221,253 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 52% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.