Cited 0 times in Scipus Cited Count

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

DC Field Value Language
dc.contributor.authorManavalan, B-
dc.contributor.authorShin, TH-
dc.contributor.authorKim, MO-
dc.contributor.authorLee, G-
dc.date.accessioned2019-11-13T00:19:03Z-
dc.date.available2019-11-13T00:19:03Z-
dc.date.issued2018-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/17072-
dc.description.abstractProinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition-transition-distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews' correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2-5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.-
dc.language.isoen-
dc.titlePIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions-
dc.typeArticle-
dc.identifier.pmid30108593-
dc.identifier.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6079197/-
dc.subject.keywordensemble learning-
dc.subject.keywordimmunotherapy-
dc.subject.keywordmachine learning-
dc.subject.keywordproinflammatory peptide-
dc.subject.keywordrandom forest-
dc.contributor.affiliatedAuthorBalachandran, Manavalan-
dc.contributor.affiliatedAuthor이, 광-
dc.type.localJournal Papers-
dc.identifier.doi10.3389/fimmu.2018.01783-
dc.citation.titleFrontiers in immunology-
dc.citation.volume9-
dc.citation.date2018-
dc.citation.startPage1783-
dc.citation.endPage1783-
dc.identifier.bibliographicCitationFrontiers in immunology, 9. : 1783-1783, 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:
30108593.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