Cited 0 times in Scipus Cited Count

iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction

DC Field Value Language
dc.contributor.authorManavalan, B-
dc.contributor.authorGovindaraj, RG-
dc.contributor.authorShin, TH-
dc.contributor.authorKim, MO-
dc.contributor.authorLee, G-
dc.date.accessioned2019-11-13T00:18:58Z-
dc.date.available2019-11-13T00:18:58Z-
dc.date.issued2018-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/17061-
dc.description.abstractIdentification of B-cell epitopes (BCEs) is a fundamental step for epitope-based vaccine development, antibody production, and disease prevention and diagnosis. Due to the avalanche of protein sequence data discovered in postgenomic age, it is essential to develop an automated computational method to enable fast and accurate identification of novel BCEs within vast number of candidate proteins and peptides. Although several computational methods have been developed, their accuracy is unreliable. Thus, developing a reliable model with significant prediction improvements is highly desirable. In this study, we first constructed a non-redundant data set of 5,550 experimentally validated BCEs and 6,893 non-BCEs from the Immune Epitope Database. We then developed a novel ensemble learning framework for improved linear BCE predictor called iBCE-EL, a fusion of two independent predictors, namely, extremely randomized tree (ERT) and gradient boosting (GB) classifiers, which, respectively, uses a combination of physicochemical properties (PCP) and amino acid composition and a combination of dipeptide and PCP as input features. Cross-validation analysis on a benchmarking data set showed that iBCE-EL performed better than individual classifiers (ERT and GB), with a Matthews correlation coefficient (MCC) of 0.454. Furthermore, we evaluated the performance of iBCE-EL on the independent data set. Results show that iBCE-EL significantly outperformed the state-of-the-art method with an MCC of 0.463. To the best of our knowledge, iBCE-EL is the first ensemble method for linear BCEs prediction. iBCE-EL was implemented in a web-based platform, which is available at http://thegleelab.org/iBCE-EL. iBCE-EL contains two prediction modes. The first one identifying peptide sequences as BCEs or non-BCEs, while later one is aimed at providing users with the option of mining potential BCEs from protein sequences.-
dc.formatapplication/pdf-
dc.language.isoen-
dc.titleiBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction-
dc.typeArticle-
dc.identifier.pmid30100904-
dc.identifier.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072840/-
dc.subject.keywordB-cell epitope-
dc.subject.keywordensemble learning-
dc.subject.keywordextremely randomized tree-
dc.subject.keywordgradient boosting-
dc.subject.keywordimmunotherapy-
dc.contributor.affiliatedAuthorBalachandran, Manavalan-
dc.contributor.affiliatedAuthor이, 광-
dc.type.localJournal Papers-
dc.identifier.doi10.3389/fimmu.2018.01695-
dc.citation.titleFrontiers in immunology-
dc.citation.volume9-
dc.citation.date2018-
dc.citation.startPage1695-
dc.citation.endPage1695-
dc.identifier.bibliographicCitationFrontiers in immunology, 9. : 1695-1695, 2018-
dc.identifier.eissn1664-3224-
dc.relation.journalidJ016643224-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Physiology
Files in This Item:
30100904.pdfDownload

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse