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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data
|
|---|---|
| Published in |
BMC Bioinformatics, November 2013
|
| DOI | 10.1186/1471-2105-14-340 |
| Pubmed ID | |
| Authors |
Natalie L Catlett, Anthony J Bargnesi, Stephen Ungerer, Toby Seagaran, William Ladd, Keith O Elliston, Dexter Pratt |
| Abstract |
Gene expression profiling and other genome-scale measurement technologies provide comprehensive information about molecular changes resulting from a chemical or genetic perturbation, or disease state. A critical challenge is the development of methods to interpret these large-scale data sets to identify specific biological mechanisms that can provide experimentally verifiable hypotheses and lead to the understanding of disease and drug action. |
X Demographics
The data shown below were collected from the profiles of 9 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 | 3 | 33% |
| Spain | 1 | 11% |
| Italy | 1 | 11% |
| Unknown | 4 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 56% |
| Scientists | 3 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 5% |
| Germany | 4 | 3% |
| United Kingdom | 3 | 3% |
| Netherlands | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 103 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 31 | 26% |
| Student > Ph. D. Student | 27 | 23% |
| Student > Master | 19 | 16% |
| Other | 11 | 9% |
| Student > Bachelor | 7 | 6% |
| Other | 11 | 9% |
| Unknown | 12 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 36 | 31% |
| Computer Science | 20 | 17% |
| Biochemistry, Genetics and Molecular Biology | 17 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 5% |
| Engineering | 6 | 5% |
| Other | 13 | 11% |
| Unknown | 20 | 17% |
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 27 December 2022.
All research outputs
#6,642,268
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#2,463
of 7,454 outputs
Outputs of similar age
#75,181
of 307,876 outputs
Outputs of similar age from BMC Bioinformatics
#31
of 102 outputs
Altmetric has tracked 23,881,329 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 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 66% 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 307,876 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 75% of its contemporaries.
We're also able to compare this research output to 102 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 70% of its contemporaries.