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Gene function classification using Bayesian models with hierarchy-based priors

Overview of attention for article published in BMC Bioinformatics, October 2006
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
28 Mendeley
citeulike
2 CiteULike
connotea
2 Connotea
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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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 14%
France 1 4%
Canada 1 4%
Brazil 1 4%
Unknown 21 75%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 21%
Professor > Associate Professor 6 21%
Student > Ph. D. Student 5 18%
Student > Doctoral Student 3 11%
Researcher 3 11%
Other 3 11%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 29%
Computer Science 7 25%
Engineering 4 14%
Mathematics 3 11%
Medicine and Dentistry 2 7%
Other 2 7%
Unknown 2 7%

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 10 October 2009.
All research outputs
#3,464,293
of 12,128,508 outputs
Outputs from BMC Bioinformatics
#1,660
of 4,410 outputs
Outputs of similar age
#81,350
of 275,165 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 141 outputs
Altmetric has tracked 12,128,508 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,410 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 52% 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 275,165 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 66% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.