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A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

Overview of attention for article published in International Journal of Molecular Sciences, January 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

twitter
21 tweeters
facebook
1 Facebook page

Citations

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11 Dimensions

Readers on

mendeley
46 Mendeley
Title
A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces
Published in
International Journal of Molecular Sciences, January 2016
DOI 10.3390/ijms17081215
Pubmed ID
Authors

Rita Melo, Melo, Rita, Fieldhouse, Robert, Melo, André, Correia, João D G, Cordeiro, Maria Natália D S, Gümüş, Zeynep H, Costa, Joaquim, Bonvin, Alexandre M J J, Moreira, Irina S, Robert Fieldhouse, André Melo, João Correia, Maria Cordeiro, Zeynep Gümüş, Joaquim Costa, Alexandre Bonvin, Irina Moreira

Abstract

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 22%
Researcher 9 20%
Unspecified 6 13%
Professor > Associate Professor 5 11%
Student > Ph. D. Student 4 9%
Other 12 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 24%
Agricultural and Biological Sciences 7 15%
Chemistry 7 15%
Unspecified 6 13%
Computer Science 6 13%
Other 9 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 27 July 2017.
All research outputs
#1,034,882
of 11,728,725 outputs
Outputs from International Journal of Molecular Sciences
#251
of 7,582 outputs
Outputs of similar age
#35,473
of 265,728 outputs
Outputs of similar age from International Journal of Molecular Sciences
#15
of 382 outputs
Altmetric has tracked 11,728,725 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,582 research outputs from this source. They receive a mean Attention Score of 2.6. This one has done particularly well, scoring higher than 96% 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 265,728 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 86% of its contemporaries.
We're also able to compare this research output to 382 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.