↓ Skip to main content

Prediction of 8-state protein secondary structures by a novel deep learning architecture

Overview of attention for article published in BMC Bioinformatics, August 2018
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
38 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
Prediction of 8-state protein secondary structures by a novel deep learning architecture
Published in
BMC Bioinformatics, August 2018
DOI 10.1186/s12859-018-2280-5
Pubmed ID
Authors

Buzhong Zhang, Jinyan Li, Qiang Lü

Abstract

Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure-based properties analysis. We present a novel deep learning architecture which exploits an integrative synergy of prediction by a convolutional neural network, residual network, and bidirectional recurrent neural network to improve the performance of protein secondary structure prediction. A local block comprised of convolutional filters and original input is designed for capturing local sequence features. The subsequent bidirectional recurrent neural network consisting of gated recurrent units can capture global context features. Furthermore, the residual network can improve the information flow between the hidden layers and the cascaded recurrent neural network. Our proposed deep network achieved 71.4% accuracy on the benchmark CB513 dataset for the 8-state prediction; and the ensemble learning by our model achieved 74% accuracy. Our model generalization capability is also evaluated on other three independent datasets CASP10, CASP11 and CASP12 for both 8- and 3-state prediction. These prediction performances are superior to the state-of-the-art methods. Our experiment demonstrates that it is a valuable method for predicting protein secondary structure, and capturing local and global features concurrently is very useful in deep learning.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 34%
Student > Bachelor 7 18%
Researcher 6 16%
Unspecified 3 8%
Student > Master 3 8%
Other 6 16%
Readers by discipline Count As %
Computer Science 16 42%
Biochemistry, Genetics and Molecular Biology 7 18%
Unspecified 5 13%
Chemistry 3 8%
Chemical Engineering 1 3%
Other 6 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 07 August 2018.
All research outputs
#6,729,936
of 13,337,884 outputs
Outputs from BMC Bioinformatics
#2,329
of 4,994 outputs
Outputs of similar age
#106,634
of 268,424 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 16 outputs
Altmetric has tracked 13,337,884 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,994 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 52% 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 268,424 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.