About: Deep learning methods improve linear B-cell epitope prediction

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About: Deep learning methods improve linear B-cell epitope prediction Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Deep learning methods improve linear B-cell epitope prediction
Creator	Liu, Tao Li, Wuju Shi, Kaiwen
Source	PMC
abstract	BACKGROUND: B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes. RESULTS: After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available. CONCLUSIONS: We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1186/s13040-020-00211-0
has license	cc-by
sha1sum (hex)	19ab9cd6ea3d7bff1a704bd7a7a1b03f5bf283de
schema:url	https://doi.org/10.1186/s13040-020-00211-0
resource representing a document's title	Deep learning methods improve linear B-cell epitope prediction
has PubMed Central identifier	PMC7371472
schema:publication	BioData Min
resource representing a document's body	covid:19ab9cd6ea3d7bff1a704bd7a7a1b03f5bf283de#body_text
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