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DRREP: deep ridge regressed epitope predictor

Overview of attention for article published in BMC Genomics, October 2017
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Title
DRREP: deep ridge regressed epitope predictor
Published in
BMC Genomics, October 2017
DOI 10.1186/s12864-017-4024-8
Pubmed ID
Authors

Gene Sher, Degui Zhi, Shaojie Zhang

Abstract

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 24%
Student > Ph. D. Student 7 16%
Other 4 9%
Student > Master 4 9%
Student > Doctoral Student 3 7%
Other 5 11%
Unknown 11 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 18%
Biochemistry, Genetics and Molecular Biology 7 16%
Computer Science 6 13%
Medicine and Dentistry 5 11%
Immunology and Microbiology 2 4%
Other 3 7%
Unknown 14 31%

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 05 October 2017.
All research outputs
#10,522,318
of 11,874,340 outputs
Outputs from BMC Genomics
#6,185
of 7,040 outputs
Outputs of similar age
#229,854
of 272,600 outputs
Outputs of similar age from BMC Genomics
#78
of 84 outputs
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