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Deep learning methods for protein torsion angle prediction

Overview of attention for article published in BMC Bioinformatics, September 2017
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  • Above-average Attention Score compared to outputs of the same age (58th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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6 tweeters

Citations

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20 Dimensions

Readers on

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66 Mendeley
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2 CiteULike
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Title
Deep learning methods for protein torsion angle prediction
Published in
BMC Bioinformatics, September 2017
DOI 10.1186/s12859-017-1834-2
Pubmed ID
Authors

Haiou Li, Jie Hou, Badri Adhikari, Qiang Lyu, Jianlin Cheng

Abstract

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 27%
Student > Master 10 15%
Student > Bachelor 8 12%
Researcher 7 11%
Student > Doctoral Student 4 6%
Other 9 14%
Unknown 10 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 19 29%
Computer Science 13 20%
Chemistry 7 11%
Engineering 4 6%
Agricultural and Biological Sciences 3 5%
Other 9 14%
Unknown 11 17%

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 28 November 2017.
All research outputs
#6,464,194
of 12,211,623 outputs
Outputs from BMC Bioinformatics
#2,336
of 4,442 outputs
Outputs of similar age
#108,740
of 268,283 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 95 outputs
Altmetric has tracked 12,211,623 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,442 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 45th percentile – i.e., 45% 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 268,283 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 58% of its contemporaries.
We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.