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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Knowledge-driven genomic interactions: an application in ovarian cancer
|
|---|---|
| Published in |
BioData Mining, September 2014
|
| DOI | 10.1186/1756-0381-7-20 |
| Pubmed ID | |
| Authors |
Dokyoon Kim, Ruowang Li, Scott M Dudek, Alex T Frase, Sarah A Pendergrass, Marylyn D Ritchie |
| Abstract |
Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner. |
X Demographics
The data shown below were collected from the profiles of 5 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 | 4 | 80% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 6% |
| United Kingdom | 1 | 2% |
| Ukraine | 1 | 2% |
| Ghana | 1 | 2% |
| Unknown | 42 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 31% |
| Researcher | 15 | 31% |
| Student > Doctoral Student | 7 | 15% |
| Student > Master | 5 | 10% |
| Professor > Associate Professor | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 1 | 2% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 25% |
| Biochemistry, Genetics and Molecular Biology | 11 | 23% |
| Computer Science | 10 | 21% |
| Medicine and Dentistry | 7 | 15% |
| Nursing and Health Professions | 1 | 2% |
| Other | 3 | 6% |
| Unknown | 4 | 8% |
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 28 October 2014.
All research outputs
#6,391,095
of 23,567,572 outputs
Outputs from BioData Mining
#132
of 313 outputs
Outputs of similar age
#61,619
of 240,067 outputs
Outputs of similar age from BioData Mining
#4
of 6 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 313 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 56% 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 240,067 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 73% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.