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Mendeley readers
Attention Score in Context
| Title |
Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!
|
|---|---|
| Published in |
Frontiers in Genetics, January 2012
|
| DOI | 10.3389/fgene.2012.00026 |
| Pubmed ID | |
| Authors |
Paola Sebastiani, Nadia Solovieff, Jenny X. Sun |
| Abstract |
One of the most popular modeling approaches to genetic risk prediction is to use a summary of risk alleles in the form of an unweighted or a weighted genetic risk score, with weights that relate to the odds for the phenotype in carriers of the individual alleles. Recent contributions have proposed the use of Bayesian classification rules using Naïve Bayes classifiers. We examine the relation between the two approaches for genetic risk prediction and show that the methods are mathematically related. In addition, we study the properties of the two approaches and describe how they can be generalized to include various models of inheritance. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 2 | 4% |
| Unknown | 45 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 26% |
| Researcher | 8 | 17% |
| Student > Master | 5 | 11% |
| Professor | 5 | 11% |
| Student > Doctoral Student | 4 | 9% |
| Other | 3 | 6% |
| Unknown | 10 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 17% |
| Biochemistry, Genetics and Molecular Biology | 5 | 11% |
| Medicine and Dentistry | 5 | 11% |
| Mathematics | 4 | 9% |
| Computer Science | 4 | 9% |
| Other | 9 | 19% |
| Unknown | 12 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 24 December 2020.
All research outputs
#6,381,113
of 22,675,759 outputs
Outputs from Frontiers in Genetics
#1,947
of 11,737 outputs
Outputs of similar age
#57,628
of 244,088 outputs
Outputs of similar age from Frontiers in Genetics
#56
of 255 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 11,737 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 83% 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 244,088 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 74% of its contemporaries.
We're also able to compare this research output to 255 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.