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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Markov Chain Ontology Analysis (MCOA)
|
|---|---|
| Published in |
BMC Bioinformatics, February 2012
|
| DOI | 10.1186/1471-2105-13-23 |
| Pubmed ID | |
| Authors |
H Robert Frost, Alexa T McCray |
| Abstract |
Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 9% |
| United Kingdom | 3 | 4% |
| Brazil | 2 | 3% |
| France | 2 | 3% |
| Netherlands | 1 | 1% |
| Japan | 1 | 1% |
| Spain | 1 | 1% |
| Unknown | 52 | 76% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 24 | 35% |
| Student > Ph. D. Student | 15 | 22% |
| Student > Master | 6 | 9% |
| Student > Doctoral Student | 5 | 7% |
| Student > Bachelor | 5 | 7% |
| Other | 8 | 12% |
| Unknown | 5 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 34% |
| Computer Science | 15 | 22% |
| Biochemistry, Genetics and Molecular Biology | 5 | 7% |
| Engineering | 4 | 6% |
| Psychology | 2 | 3% |
| Other | 10 | 15% |
| Unknown | 9 | 13% |
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 30 October 2014.
All research outputs
#6,245,187
of 22,662,201 outputs
Outputs from BMC Bioinformatics
#2,400
of 7,242 outputs
Outputs of similar age
#57,410
of 247,565 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 57 outputs
Altmetric has tracked 22,662,201 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 7,242 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 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 247,565 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 76% of its contemporaries.
We're also able to compare this research output to 57 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 68% of its contemporaries.