↓ Skip to main content

COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator

Overview of attention for article published in BMC Bioinformatics, December 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
26 Mendeley
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.
Title
COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator
Published in
BMC Bioinformatics, December 2016
DOI 10.1186/s12859-016-1400-3
Pubmed ID
Authors

Reda Rawi, Raghvendra Mall, Khalid Kunji, Mohammed El Anbari, Michael Aupetit, Ehsan Ullah, Halima Bensmail

Abstract

The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso. Using the original PSICOV benchmark dataset, COUSCOus achieves mean accuracies of 0.74, 0.62 and 0.55 for the top L/10 predicted long, medium and short range contacts, respectively. In addition, COUSCOus attains mean areas under the precision-recall curves of 0.25, 0.29 and 0.30 for long, medium and short contacts and outperforms PSICOV. We also observed that COUSCOus outperforms PSICOV w.r.t. Matthew's correlation coefficient criterion on full list of residue contacts. Furthermore, COUSCOus achieves on average 10% more gain in prediction accuracy compared to PSICOV on an independent test set composed of CASP11 protein targets. Finally, we showed that when using a simple random forest meta-classifier, by combining contact detecting techniques and sequence derived features, PSICOV predictions should be replaced by the more accurate COUSCOus predictions. We conclude that the consideration of superior covariance shrinkage approaches will boost several research fields that apply the GLasso procedure, amongst the presented one of residue-residue contact prediction as well as fields such as gene network reconstruction.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 35%
Student > Ph. D. Student 6 23%
Student > Bachelor 3 12%
Student > Master 3 12%
Other 1 4%
Other 2 8%
Unknown 2 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 31%
Biochemistry, Genetics and Molecular Biology 6 23%
Computer Science 4 15%
Social Sciences 1 4%
Medicine and Dentistry 1 4%
Other 0 0%
Unknown 6 23%

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 18 December 2016.
All research outputs
#6,935,558
of 11,204,206 outputs
Outputs from BMC Bioinformatics
#2,913
of 4,189 outputs
Outputs of similar age
#173,717
of 316,888 outputs
Outputs of similar age from BMC Bioinformatics
#72
of 135 outputs
Altmetric has tracked 11,204,206 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,189 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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 316,888 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.