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An algorithm to parse segment packing in predicted protein contact maps

Overview of attention for article published in Algorithms for Molecular Biology, June 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#38 of 230)
  • Good Attention Score compared to outputs of the same age (75th percentile)

Mentioned by

twitter
8 tweeters

Citations

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7 Dimensions

Readers on

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9 Mendeley
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Title
An algorithm to parse segment packing in predicted protein contact maps
Published in
Algorithms for Molecular Biology, June 2016
DOI 10.1186/s13015-016-0080-x
Pubmed ID
Authors

William R. Taylor

Abstract

The analysis of correlation in alignments generates a matrix of predicted contacts between positions in the structure and while these can arise for many reasons, the simplest explanation is that the pair of residues are in contact in a three-dimensional structure and are affecting each others selection pressure. To analyse these data, A dynamic programming algorithm was developed for parsing secondary structure interactions in predicted contact maps. The non-local nature of the constraints required an iterated approach (using a "frozen approximation") but with good starting definitions, a single pass was usually sufficient. The method was shown to be effective when applied to the transmembrane class of protein and error tolerant even when the signal becomes degraded. In the globular class of protein, where the extent of interactions are more limited and more complex, the algorithm still behaved well, classifying most of the important interactions correctly in both a small and a large test case. For the larger protein, this involved examples of the algorithm apportioning parts of a single large secondary structure element between two different interactions. It is expected that the method will be useful as a pre-processor to coarse-grained modelling methods to extend the range of protein tertiary structure prediction to larger proteins or to data that is currently too 'noisy' to be used by current residue-based methods.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 33%
Student > Ph. D. Student 2 22%
Librarian 1 11%
Student > Master 1 11%
Student > Bachelor 1 11%
Other 1 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 44%
Biochemistry, Genetics and Molecular Biology 3 33%
Arts and Humanities 1 11%
Medicine and Dentistry 1 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 30 November 2017.
All research outputs
#3,843,214
of 16,256,075 outputs
Outputs from Algorithms for Molecular Biology
#38
of 230 outputs
Outputs of similar age
#66,716
of 267,606 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 2 outputs
Altmetric has tracked 16,256,075 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 230 research outputs from this source. They receive a mean Attention Score of 2.9. 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 267,606 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 75% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them