{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T06:22:42Z","timestamp":1781245362792,"version":"3.54.1"},"reference-count":67,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides\/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https:\/\/webs.iiitd.edu.in\/raghava\/il6pred\/).<\/jats:p>","DOI":"10.1093\/bib\/bbaa259","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T19:12:49Z","timestamp":1600110769000},"page":"936-945","source":"Crossref","is-referenced-by-count":127,"title":["Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19"],"prefix":"10.1093","volume":"22","author":[{"given":"Anjali","family":"Dhall","sequence":"first","affiliation":[{"name":"Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumeet","family":"Patiyal","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neelam","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salman Sadullah","family":"Usmani","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8902-2876","authenticated-orcid":false,"given":"Gajendra P S","family":"Raghava","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, 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