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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Gene-Category Analysis
|
|---|---|
| Chapter number | 13 |
| Book title |
The Gene Ontology Handbook
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-3743-1_13 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3741-7, 978-1-4939-3743-1
|
| Authors |
Sebastian Bauer |
| Editors |
Christophe Dessimoz, Nives Škunca |
| Abstract |
Gene-category analysis is one important knowledge integration approach in biomedical sciences that combines knowledge bases such as Gene Ontology with lists of genes or their products, which are often the result of high-throughput experiments, gained from either wet-lab or synthetic experiments. In this chapter, we will motivate this class of analyses and describe an often used variant that is based on Fisher's exact test. We show that this approach has some problems in the context of Gene Ontology of which users should be aware. We then describe some more recent algorithms that try to address some of the shortcomings of the standard approach. |
X Demographics
The data shown below were collected from the profiles of 10 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 | 20% |
| United States | 1 | 10% |
| Spain | 1 | 10% |
| Korea, Republic of | 1 | 10% |
| Switzerland | 1 | 10% |
| Unknown | 4 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 70% |
| Members of the public | 2 | 20% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 13% |
| Unknown | 7 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 3 | 38% |
| Researcher | 2 | 25% |
| Student > Ph. D. Student | 1 | 13% |
| Professor > Associate Professor | 1 | 13% |
| Unknown | 1 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 25% |
| Agricultural and Biological Sciences | 2 | 25% |
| Mathematics | 1 | 13% |
| Social Sciences | 1 | 13% |
| Neuroscience | 1 | 13% |
| Other | 0 | 0% |
| Unknown | 1 | 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 28 November 2016.
All research outputs
#6,068,873
of 22,899,952 outputs
Outputs from Methods in molecular biology
#1,798
of 13,134 outputs
Outputs of similar age
#113,401
of 420,444 outputs
Outputs of similar age from Methods in molecular biology
#206
of 1,074 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 13,134 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 86% 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 420,444 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 72% of its contemporaries.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.