B-Cell Linear Epitope Prediction Using Transformer Encoder
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Y. | - |
dc.contributor.author | Park, J. | - |
dc.contributor.author | Kwon, Junseok | - |
dc.date.accessioned | 2022-12-27T02:41:58Z | - |
dc.date.available | 2022-12-27T02:41:58Z | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59763 | - |
dc.description.abstract | When a pathogen invade a host, the antibody binds to a specific part of the pathogen's antigen and neutralizes the pathogen, thereby performing immune activities. The site of the antigen recognizable by the antibody is called epitope. Accurate prediction of epitope is very important for vaccine design, targeted therapy and understanding of immune system. Since the assay experiment to determine the epitope of the antigen is usually an extensive laboratory work, serveral studies have been conducted to predict the B-Cell epitope quickly and conveniently by using machine learning. In this paper, we propose a novel deep neural network based on transformer encoder and 1-dimensional ResNet to predict B-cell linear epitopes. © 2022 IEEE. | - |
dc.format.extent | 3 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | B-Cell Linear Epitope Prediction Using Transformer Encoder | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICTC55196.2022.9952973 | - |
dc.identifier.bibliographicCitation | International Conference on ICT Convergence, v.2022-October, pp 1091 - 1093 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85143256389 | - |
dc.citation.endPage | 1093 | - |
dc.citation.startPage | 1091 | - |
dc.citation.title | International Conference on ICT Convergence | - |
dc.citation.volume | 2022-October | - |
dc.type.docType | Conference Paper | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Antibody | - |
dc.subject.keywordAuthor | Linear epitope | - |
dc.subject.keywordAuthor | Transformer | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.