{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T02:48:26Z","timestamp":1769827706860,"version":"3.49.0"},"reference-count":76,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University","doi-asserted-by":"publisher","award":["RG-83-130-38"],"award-info":[{"award-number":["RG-83-130-38"]}],"id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>B-cell epitopes have the capability to recognize and attach to the surface of antigen receptors to stimulate the immune system against pathogens. Identification of B-cell epitopes from antigens has a great significance in several biomedical and biotechnological applications, provides support in the development of therapeutics, design and development of an epitope-based vaccine and antibody production. However, the identification of epitopes with experimental mapping approaches is a challenging job and usually requires extensive laboratory efforts. However, considerable efforts have been placed for the identification of epitopes using computational methods in the recent past but deprived of considerable achievements. In this study, we present LBCEPred, a python-based web-tool (http:\/\/lbcepred.pythonanywhere.com\/), build with random forest classifier and statistical moment-based descriptors to predict the B-cell epitopes from the protein sequences. LBECPred outperforms all sequence-based available models that are currently in use for the B-cell epitopes prediction, with 0.868 accuracy value and 0.934 area under the curve. Moreover, the prediction performance of proposed models compared to other state-of-the-art models is 56.3% higher on average for Mathews Correlation Coefficient. LBCEPred is easy to use tool even for novice users and has also shown the models stability and reliability, thus we believe in its significant contribution to the research community and the area of bioinformatics.<\/jats:p>","DOI":"10.1093\/bib\/bbac035","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T04:11:55Z","timestamp":1643256715000},"source":"Crossref","is-referenced-by-count":35,"title":["LBCEPred: a machine learning model to predict linear B-cell epitopes"],"prefix":"10.1093","volume":"23","author":[{"given":"Wajdi","family":"Alghamdi","sequence":"first","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 80221, Jeddah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Attique","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Management and Technology, Lahore, 54000, Pakistan"},{"name":"Department of Information Technology, University of Gujrat, Gujrat, 50700, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ebraheem","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malik Zaka","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2482-275X","authenticated-orcid":false,"given":"Yaser Daanial","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Management and Technology, Lahore, 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"2022051813065522100_ref1","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1093\/bioinformatics\/btaa773","article-title":"Epidope: a deep neural network for linear B-cell epitope prediction","volume":"37","author":"Collatz","year":"2021","journal-title":"Bioinformatics"},{"key":"2022051813065522100_ref2","doi-asserted-by":"crossref","DOI":"10.1155\/2017\/2680160","article-title":"Fundamentals and methods for T- and B-cell epitope prediction","volume":"2017","author":"Sanchez-Trincado","year":"2017","journal-title":"J Immunol Res"},{"key":"2022051813065522100_ref3","article-title":"Epitope | biochemistry |","author":"Rogers","year":"2009","journal-title":"Britannica"},{"key":"2022051813065522100_ref4","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1080\/19420862.2017.1402998","article-title":"Enhancing antibody patent protection using epitope mapping information","volume":"10","author":"Deng","year":"2018","journal-title":"MAbs"},{"key":"2022051813065522100_ref5","doi-asserted-by":"crossref","first-page":"W24","DOI":"10.1093\/nar\/gkx346","article-title":"BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes","volume":"45","author":"Jespersen","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2022051813065522100_ref6","first-page":"248","article-title":"Epitope mapping: a practical approach","volume-title":"Oxford University Press","author":"Westwood","year":"2001"},{"key":"2022051813065522100_ref7","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/S1367-5931(00)00208-8","article-title":"Random-peptide libraries and antigen-fragment libraries for epitope mapping and the development of vaccines and diagnostics","volume":"5","author":"Irving","year":"2001","journal-title":"Curr Opin Chem Biol"},{"key":"2022051813065522100_ref8","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s00251-005-0803-5","article-title":"The design and implementation of the immune epitope database and analysis resource","volume":"57","author":"Peters","year":"2005","journal-title":"Immunogenetics"},{"key":"2022051813065522100_ref9","doi-asserted-by":"crossref","first-page":"531","DOI":"10.2174\/092986707780059698","article-title":"Synthetic peptides for the immunodiagnosis of human diseases","volume":"14","author":"Gomara","year":"2007","journal-title":"Curr Med Chem"},{"key":"2022051813065522100_ref10","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1006\/mthe.1999.0001","article-title":"Type-specific epitope locations revealed by X-ray crystallographic study of adenovirus type 5 hexon","volume":"1","author":"Rux","year":"2000","journal-title":"Mol Ther"},{"key":"2022051813065522100_ref11","doi-asserted-by":"crossref","first-page":"6108","DOI":"10.1021\/ja0100120","article-title":"Group epitope mapping by saturation transfer difference NMR to identify segments of a ligand in direct contact with a protein receptor","volume":"123","author":"Mayer","year":"2001","journal-title":"J Am Chem Soc"},{"key":"2022051813065522100_ref12","doi-asserted-by":"crossref","first-page":"148570","DOI":"10.1109\/ACCESS.2020.3015792","article-title":"Prediction of therapeutic peptides using machine learning: computational models, datasets, and feature encodings","volume":"8","author":"Attique","year":"2020","journal-title":"IEEE Access"},{"key":"2022051813065522100_ref13","first-page":"148570","volume-title":"2013 4th Int. 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