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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference
|
|---|---|
| Published in |
BMC Bioinformatics, October 2011
|
| DOI | 10.1186/1471-2105-12-403 |
| Pubmed ID | |
| Authors |
Yan Jiao, Katherine Lawler, Gargi S Patel, Arnie Purushotham, Annette F Jones, Anita Grigoriadis, Andrew Tutt, Tony Ng, Andrew E Teschendorff |
| Abstract |
Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression) with independent and highly curated structural interaction data (e.g protein interaction networks) or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 3% |
| Germany | 1 | 1% |
| Sweden | 1 | 1% |
| United Kingdom | 1 | 1% |
| Mexico | 1 | 1% |
| Russia | 1 | 1% |
| United States | 1 | 1% |
| Luxembourg | 1 | 1% |
| Unknown | 68 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 27 | 35% |
| Student > Ph. D. Student | 17 | 22% |
| Professor | 7 | 9% |
| Student > Bachelor | 4 | 5% |
| Professor > Associate Professor | 4 | 5% |
| Other | 9 | 12% |
| Unknown | 9 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 25 | 32% |
| Computer Science | 13 | 17% |
| Biochemistry, Genetics and Molecular Biology | 9 | 12% |
| Medicine and Dentistry | 8 | 10% |
| Mathematics | 4 | 5% |
| Other | 8 | 10% |
| Unknown | 10 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 31 May 2015.
All research outputs
#6,375,523
of 22,655,397 outputs
Outputs from BMC Bioinformatics
#2,470
of 7,236 outputs
Outputs of similar age
#38,620
of 139,261 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 101 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 64% 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 139,261 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 70% of its contemporaries.
We're also able to compare this research output to 101 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 58% of its contemporaries.