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 |
MiningABs: mining associated biomarkers across multi-connected gene expression datasets
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-173 |
| Pubmed ID | |
| Authors |
Chun-Pei Cheng, Christopher DeBoever, Kelly A Frazer, Yu-Cheng Liu, Vincent S Tseng |
| Abstract |
Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. Therefore, in this study, we present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms. |
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 % |
|---|---|---|
| Norway | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 1 | 25% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 2% |
| Denmark | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 42 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 36% |
| Student > Ph. D. Student | 11 | 24% |
| Student > Bachelor | 4 | 9% |
| Student > Master | 4 | 9% |
| Other | 3 | 7% |
| Other | 4 | 9% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 24% |
| Computer Science | 9 | 20% |
| Agricultural and Biological Sciences | 8 | 18% |
| Biochemistry, Genetics and Molecular Biology | 7 | 16% |
| Mathematics | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 6 | 13% |
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 12 June 2014.
All research outputs
#12,900,070
of 22,757,090 outputs
Outputs from BMC Bioinformatics
#3,783
of 7,272 outputs
Outputs of similar age
#105,707
of 228,706 outputs
Outputs of similar age from BMC Bioinformatics
#69
of 157 outputs
Altmetric has tracked 22,757,090 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 7,272 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% 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 228,706 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 53% of its contemporaries.
We're also able to compare this research output to 157 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 53% of its contemporaries.