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Sequence-based prediction of protein protein interaction using a deep-learning algorithm

Overview of attention for article published in BMC Bioinformatics, May 2017
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
46 tweeters

Citations

dimensions_citation
155 Dimensions

Readers on

mendeley
322 Mendeley
citeulike
2 CiteULike
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Title
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Published in
BMC Bioinformatics, May 2017
DOI 10.1186/s12859-017-1700-2
Pubmed ID
Authors

Tanlin Sun, Bo Zhou, Luhua Lai, Jianfeng Pei

Abstract

Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

Twitter Demographics

The data shown below were collected from the profiles of 46 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 322 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 <1%
Unknown 321 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 69 21%
Student > Bachelor 47 15%
Researcher 47 15%
Student > Master 44 14%
Student > Doctoral Student 13 4%
Other 45 14%
Unknown 57 18%
Readers by discipline Count As %
Computer Science 84 26%
Biochemistry, Genetics and Molecular Biology 69 21%
Agricultural and Biological Sciences 40 12%
Engineering 11 3%
Chemistry 11 3%
Other 38 12%
Unknown 69 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 25 March 2019.
All research outputs
#1,053,940
of 17,125,476 outputs
Outputs from BMC Bioinformatics
#212
of 6,086 outputs
Outputs of similar age
#26,814
of 276,419 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 11 outputs
Altmetric has tracked 17,125,476 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,086 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 276,419 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 11 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 99% of its contemporaries.