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Attention Score in Context
| Title |
Fast MCMC sampling for hidden markov models to determine copy number variations
|
|---|---|
| Published in |
BMC Bioinformatics, November 2011
|
| DOI | 10.1186/1471-2105-12-428 |
| Pubmed ID | |
| Authors |
Md Pavel Mahmud, Alexander Schliep |
| Abstract |
Hidden Markov Models (HMM) are often used for analyzing Comparative Genomic Hybridization (CGH) data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi algorithm. This introduces considerable uncertainty in the segmentation, which can be avoided with Bayesian approaches integrating out parameters using Markov Chain Monte Carlo (MCMC) sampling. While the advantages of Bayesian approaches have been clearly demonstrated, the likelihood based approaches are still preferred in practice for their lower running times; datasets coming from high-density arrays and next generation sequencing amplify these problems. |
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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 12% |
| Germany | 1 | 2% |
| Unknown | 36 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 38% |
| Student > Ph. D. Student | 10 | 24% |
| Student > Master | 3 | 7% |
| Student > Postgraduate | 2 | 5% |
| Professor > Associate Professor | 2 | 5% |
| Other | 5 | 12% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 36% |
| Computer Science | 7 | 17% |
| Engineering | 4 | 10% |
| Mathematics | 2 | 5% |
| Business, Management and Accounting | 2 | 5% |
| Other | 8 | 19% |
| Unknown | 4 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 16 April 2024.
All research outputs
#2,861,145
of 23,468,283 outputs
Outputs from BMC Bioinformatics
#927
of 7,393 outputs
Outputs of similar age
#15,758
of 143,415 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 115 outputs
Altmetric has tracked 23,468,283 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,393 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 done well, scoring higher than 87% 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 143,415 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.