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DeepQA: improving the estimation of single protein model quality with deep belief networks

Overview of attention for article published in BMC Bioinformatics, December 2016
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Title
DeepQA: improving the estimation of single protein model quality with deep belief networks
Published in
BMC Bioinformatics, December 2016
DOI 10.1186/s12859-016-1405-y
Pubmed ID
Authors

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng

Abstract

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Canada 1 1%
Unknown 77 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 16%
Student > Bachelor 10 13%
Researcher 8 10%
Student > Master 7 9%
Student > Doctoral Student 4 5%
Other 13 16%
Unknown 24 30%
Readers by discipline Count As %
Computer Science 16 20%
Biochemistry, Genetics and Molecular Biology 12 15%
Agricultural and Biological Sciences 11 14%
Chemistry 3 4%
Medicine and Dentistry 2 3%
Other 8 10%
Unknown 27 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 08 December 2016.
All research outputs
#19,017,658
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#6,465
of 7,418 outputs
Outputs of similar age
#308,498
of 419,434 outputs
Outputs of similar age from BMC Bioinformatics
#92
of 128 outputs
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