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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A two-step hierarchical hypothesis set testing framework, with applications to gene expression data on ordered categories
|
|---|---|
| Published in |
BMC Bioinformatics, April 2014
|
| DOI | 10.1186/1471-2105-15-108 |
| Pubmed ID | |
| Authors |
Yihan Li, Debashis Ghosh |
| Abstract |
In complex large-scale experiments, in addition to simultaneously considering a large number of features, multiple hypotheses are often being tested for each feature. This leads to a problem of multi-dimensional multiple testing. For example, in gene expression studies over ordered categories (such as time-course or dose-response experiments), interest is often in testing differential expression across several categories for each gene. In this paper, we consider a framework for testing multiple sets of hypothesis, which can be applied to a wide range of problems. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 6% |
| Unknown | 15 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 25% |
| Researcher | 4 | 25% |
| Student > Doctoral Student | 2 | 13% |
| Student > Bachelor | 2 | 13% |
| Student > Postgraduate | 2 | 13% |
| Other | 2 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 25% |
| Agricultural and Biological Sciences | 3 | 19% |
| Biochemistry, Genetics and Molecular Biology | 2 | 13% |
| Mathematics | 2 | 13% |
| Medicine and Dentistry | 2 | 13% |
| Other | 3 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 19 May 2014.
All research outputs
#17,719,424
of 22,753,345 outputs
Outputs from BMC Bioinformatics
#5,925
of 7,269 outputs
Outputs of similar age
#156,900
of 226,967 outputs
Outputs of similar age from BMC Bioinformatics
#90
of 125 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,269 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 13th percentile – i.e., 13% 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 226,967 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 125 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.