You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
A linear memory algorithm for Baum-Welch training
|
|---|---|
| Published in |
BMC Bioinformatics, September 2005
|
| DOI | 10.1186/1471-2105-6-231 |
| Pubmed ID | |
| Authors |
István Miklós, Irmtraud M Meyer |
| Abstract |
Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden Markov models in a fully automated way. It can be employed as long as a training set of annotated sequences is known, and provides a rigorous way to derive parameter values which are guaranteed to be at least locally optimal. For complex hidden Markov models such as pair hidden Markov models and very long training sequences, even the most efficient algorithms for Baum-Welch training are currently too memory-consuming. This has so far effectively prevented the automatic parameter training of hidden Markov models that are currently used for biological sequence analyses. |
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 % |
|---|---|---|
| Tunisia | 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 4% |
| France | 1 | 4% |
| Denmark | 1 | 4% |
| Greece | 1 | 4% |
| United States | 1 | 4% |
| Unknown | 23 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 32% |
| Researcher | 5 | 18% |
| Student > Doctoral Student | 3 | 11% |
| Professor > Associate Professor | 3 | 11% |
| Student > Master | 3 | 11% |
| Other | 3 | 11% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 32% |
| Agricultural and Biological Sciences | 8 | 29% |
| Engineering | 4 | 14% |
| Mathematics | 1 | 4% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 3 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 June 2016.
All research outputs
#3,946,314
of 22,707,247 outputs
Outputs from BMC Bioinformatics
#1,519
of 7,255 outputs
Outputs of similar age
#9,111
of 58,954 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 21 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,255 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 79% 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 58,954 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 84% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.