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Mendeley readers
Attention Score in Context
| Title |
Gene function classification using Bayesian models with hierarchy-based priors
|
|---|---|
| Published in |
BMC Bioinformatics, October 2006
|
| DOI | 10.1186/1471-2105-7-448 |
| Pubmed ID | |
| Authors |
Babak Shahbaba, Radford M Neal |
| Abstract |
We investigate whether annotation of gene function can be improved using a classification scheme that is aware that functional classes are organized in a hierarchy. The classifiers look at phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and an MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 13% |
| France | 1 | 3% |
| Canada | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 25 | 78% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 7 | 22% |
| Professor > Associate Professor | 6 | 19% |
| Student > Ph. D. Student | 5 | 16% |
| Researcher | 3 | 9% |
| Student > Doctoral Student | 3 | 9% |
| Other | 5 | 16% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 28% |
| Computer Science | 8 | 25% |
| Engineering | 4 | 13% |
| Mathematics | 4 | 13% |
| Medicine and Dentistry | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 3 | 9% |
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 29 November 2018.
All research outputs
#7,451,284
of 22,780,165 outputs
Outputs from BMC Bioinformatics
#3,020
of 7,277 outputs
Outputs of similar age
#23,188
of 66,307 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 44 outputs
Altmetric has tracked 22,780,165 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 7,277 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 66,307 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.