| Title |
Analyzing gene expression data in mice with the Neuro Behavior Ontology
|
|---|---|
| Published in |
Mammalian Genome, November 2013
|
| DOI | 10.1007/s00335-013-9481-z |
| Pubmed ID | |
| Authors |
Robert Hoehndorf, John M. Hancock, Nigel W. Hardy, Ann-Marie Mallon, Paul N. Schofield, Georgios V. Gkoutos |
| Abstract |
We have applied the Neuro Behavior Ontology (NBO), an ontology for the annotation of behavioral gene functions and behavioral phenotypes, to the annotation of more than 1,000 genes in the mouse that are known to play a role in behavior. These annotations can be explored by researchers interested in genes involved in particular behaviors and used computationally to provide insights into the behavioral phenotypes resulting from differences in gene expression. We developed the OntoFUNC tool and have applied it to enrichment analyses over the NBO to provide high-level behavioral interpretations of gene expression datasets. The resulting increase in the number of gene annotations facilitates the identification of behavioral or neurologic processes by assisting the formulation of hypotheses about the relationships between gene, processes, and phenotypic manifestations resulting from behavioral observations. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 7% |
| France | 1 | 4% |
| Brazil | 1 | 4% |
| Unknown | 24 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 32% |
| Student > Master | 6 | 21% |
| Student > Ph. D. Student | 5 | 18% |
| Other | 2 | 7% |
| Professor > Associate Professor | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 39% |
| Computer Science | 5 | 18% |
| Biochemistry, Genetics and Molecular Biology | 2 | 7% |
| Medicine and Dentistry | 2 | 7% |
| Engineering | 2 | 7% |
| Other | 3 | 11% |
| Unknown | 3 | 11% |
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 02 November 2013.
All research outputs
#18,353,475
of 22,729,647 outputs
Outputs from Mammalian Genome
#1,003
of 1,124 outputs
Outputs of similar age
#158,879
of 213,637 outputs
Outputs of similar age from Mammalian Genome
#6
of 6 outputs
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