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Attention Score in Context
| Title |
Fast algorithms for computing phylogenetic divergence time
|
|---|---|
| Published in |
BMC Bioinformatics, December 2017
|
| DOI | 10.1186/s12859-017-1916-1 |
| Pubmed ID | |
| Authors |
Ralph W. Crosby, Tiffani L. Williams |
| Abstract |
The inference of species divergence time is a key step in most phylogenetic studies. Methods have been available for the last ten years to perform the inference, but the performance of the methods does not yet scale well to studies with hundreds of taxa and thousands of DNA base pairs. For example a study of 349 primate taxa was estimated to require over 9 months of processing time. In this work, we present a new algorithm, AncestralAge, that significantly improves the performance of the divergence time process. As part of AncestralAge, we demonstrate a new method for the computation of phylogenetic likelihood and our experiments show a 90% improvement in likelihood computation time on the aforementioned dataset of 349 primates taxa with over 60,000 DNA base pairs. Additionally, we show that our new method for the computation of the Bayesian prior on node ages reduces the running time for this computation on the 349 taxa dataset by 99%. Through the use of these new algorithms we open up the ability to perform divergence time inference on large phylogenetic studies. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 30% |
| Student > Bachelor | 3 | 15% |
| Researcher | 3 | 15% |
| Professor | 2 | 10% |
| Student > Doctoral Student | 2 | 10% |
| Other | 2 | 10% |
| Unknown | 2 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 45% |
| Biochemistry, Genetics and Molecular Biology | 5 | 25% |
| Computer Science | 2 | 10% |
| Chemical Engineering | 1 | 5% |
| Engineering | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 2 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 December 2017.
All research outputs
#15,708,425
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#5,490
of 7,387 outputs
Outputs of similar age
#268,872
of 441,850 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 134 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.