General Information
| Description | Sequence-based linear B-cell epitope prediction method using multiple linear regression |
| Homepage | http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/ |
Publications
EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression
Background
B-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.
Results
In 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.
Conclusions
We 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/.
Related Tools
lrgpr
Published July 2014
Interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regress...
HLaffy
Published March 2016
Estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials
Hammock
Published September 2015
A hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datas...
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.
