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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Pathway-PDT: a flexible pathway analysis tool for nuclear families
|
|---|---|
| Published in |
BMC Bioinformatics, September 2013
|
| DOI | 10.1186/1471-2105-14-267 |
| Pubmed ID | |
| Authors |
Yo Son Park, Michael Schmidt, Eden R Martin, Margaret A Pericak-Vance, Ren-Hua Chung |
| Abstract |
Pathway analysis based on Genome-Wide Association Studies (GWAS) data has become popular as a secondary analysis strategy. Although many pathway analysis tools have been developed for case-control studies, there is no tool that can use all information from raw genotypes in general nuclear families. We developed Pathway-PDT, which uses the framework of Pedigree Disequilibrium Test (PDT) for general family data, to perform pathway analysis based on raw genotypes in family-based GWAS. |
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 % |
|---|---|---|
| China | 1 | 20% |
| United States | 1 | 20% |
| United Kingdom | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Members of the public | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 5% |
| Unknown | 18 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 37% |
| Student > Master | 5 | 26% |
| Student > Ph. D. Student | 4 | 21% |
| Student > Doctoral Student | 1 | 5% |
| Professor > Associate Professor | 1 | 5% |
| Other | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 37% |
| Computer Science | 6 | 32% |
| Biochemistry, Genetics and Molecular Biology | 1 | 5% |
| Economics, Econometrics and Finance | 1 | 5% |
| Psychology | 1 | 5% |
| Other | 2 | 11% |
| Unknown | 1 | 5% |
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 23 November 2017.
All research outputs
#7,431,619
of 22,719,618 outputs
Outputs from BMC Bioinformatics
#3,026
of 7,260 outputs
Outputs of similar age
#66,101
of 196,876 outputs
Outputs of similar age from BMC Bioinformatics
#37
of 92 outputs
Altmetric has tracked 22,719,618 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,260 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 50% 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 196,876 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 51% of its contemporaries.
We're also able to compare this research output to 92 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 56% of its contemporaries.