| Title |
AIGO: Towards a unified framework for the Analysis and the Inter-comparison of GO functional annotations
|
|---|---|
| Published in |
BMC Bioinformatics, November 2011
|
| DOI | 10.1186/1471-2105-12-431 |
| Pubmed ID | |
| Authors |
Michael Defoin-Platel, Matthew M Hindle, Artem Lysenko, Stephen J Powers, Dimah Z Habash, Christopher J Rawlings, Mansoor Saqi |
| Abstract |
In response to the rapid growth of available genome sequences, efforts have been made to develop automatic inference methods to functionally characterize them. Pipelines that infer functional annotation are now routinely used to produce new annotations at a genome scale and for a broad variety of species. These pipelines differ widely in their inference algorithms, confidence thresholds and data sources for reasoning. This heterogeneity makes a comparison of the relative merits of each approach extremely complex. The evaluation of the quality of the resultant annotations is also challenging given there is often no existing gold-standard against which to evaluate precision and recall. |
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 % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 7% |
| France | 1 | 2% |
| Brazil | 1 | 2% |
| Malaysia | 1 | 2% |
| South Africa | 1 | 2% |
| Belgium | 1 | 2% |
| Japan | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 34 | 77% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 34% |
| Student > Ph. D. Student | 11 | 25% |
| Other | 3 | 7% |
| Student > Master | 3 | 7% |
| Student > Postgraduate | 3 | 7% |
| Other | 4 | 9% |
| Unknown | 5 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 61% |
| Computer Science | 6 | 14% |
| Biochemistry, Genetics and Molecular Biology | 4 | 9% |
| Mathematics | 2 | 5% |
| Unknown | 5 | 11% |
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 26 November 2011.
All research outputs
#7,410,276
of 22,656,971 outputs
Outputs from BMC Bioinformatics
#3,022
of 7,236 outputs
Outputs of similar age
#47,358
of 141,801 outputs
Outputs of similar age from BMC Bioinformatics
#53
of 117 outputs
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