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EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression

Overview of attention for article published in BMC Bioinformatics, December 2014
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Citations

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Title
EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0414-y
Pubmed ID
Authors

Yao Lian, Meng Ge, Xian-Ming Pan

Abstract

BackgroundB-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task.ResultsIn this work, based on the antigen¿s primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728.ConclusionsWe have presented a reliable method for the identification of linear B cell epitope using antigen¿s primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
Peru 1 2%
Unknown 46 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 25%
Student > Ph. D. Student 9 19%
Student > Master 8 17%
Other 4 8%
Researcher 4 8%
Other 6 13%
Unknown 5 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 21%
Agricultural and Biological Sciences 9 19%
Computer Science 8 17%
Engineering 3 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 7 15%
Unknown 9 19%

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 20 December 2014.
All research outputs
#4,628,189
of 6,256,786 outputs
Outputs from BMC Bioinformatics
#2,634
of 3,132 outputs
Outputs of similar age
#127,296
of 191,673 outputs
Outputs of similar age from BMC Bioinformatics
#129
of 155 outputs
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