An Improved Method for Predicting Linear B-cell Epitope Using Deep Maxout Networks / 生物医学与环境科学(英文)
Biomedical and Environmental Sciences
; (12): 460-463, 2015.
Article
in En
| WPRIM
| ID: wpr-264561
Responsible library:
WPRO
ABSTRACT
To establish a relation between an protein amino acid sequence and its tendencies to generate antibody response, and to investigate an improved in silico method for linear B-cell epitope (LBE) prediction. We present a sequence-based LBE predictor developed using deep maxout network (DMN) with dropout training techniques. A graphics processing unit (GPU) was used to reduce the training time of the model. A 10-fold cross-validation test on a large, non-redundant and experimentally verified dataset (Lbtope_Fixed_ non_redundant) was performed to evaluate the performance. DMN-LBE achieved an accuracy of 68.33% and an area under the receiver operating characteristic curve (AUC) of 0.743, outperforming other prediction methods in the field. A web server, DMN-LBE, of the improved prediction model has been provided for public free use. We anticipate that DMN-LBE will be beneficial to vaccine development, antibody production, disease diagnosis, and therapy.
Full text:
1
Index:
WPRIM
Main subject:
Chemistry
/
ROC Curve
/
Amino Acid Sequence
/
Epitopes, B-Lymphocyte
/
Computational Biology
/
Allergy and Immunology
/
Methods
Type of study:
Prognostic_studies
Language:
En
Journal:
Biomedical and Environmental Sciences
Year:
2015
Type:
Article