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

Proinflammatory 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 therapie...

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Autores principales: Manavalan, Balachandran, Shin, Tae Hwan, Kim, Myeong Ok, Lee, Gwang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6079197/
https://www.ncbi.nlm.nih.gov/pubmed/30108593
http://dx.doi.org/10.3389/fimmu.2018.01783
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author Manavalan, Balachandran
Shin, Tae Hwan
Kim, Myeong Ok
Lee, Gwang
author_facet Manavalan, Balachandran
Shin, Tae Hwan
Kim, Myeong Ok
Lee, Gwang
author_sort Manavalan, Balachandran
collection PubMed
description Proinflammatory 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.
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spelling pubmed-60791972018-08-14 PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions Manavalan, Balachandran Shin, Tae Hwan Kim, Myeong Ok Lee, Gwang Front Immunol Immunology Proinflammatory 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. Frontiers Media S.A. 2018-07-31 /pmc/articles/PMC6079197/ /pubmed/30108593 http://dx.doi.org/10.3389/fimmu.2018.01783 Text en Copyright © 2018 Manavalan, Shin, Kim and Lee. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Manavalan, Balachandran
Shin, Tae Hwan
Kim, Myeong Ok
Lee, Gwang
PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
title PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
title_full PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
title_fullStr PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
title_full_unstemmed PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
title_short PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
title_sort pip-el: a new ensemble learning method for improved proinflammatory peptide predictions
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6079197/
https://www.ncbi.nlm.nih.gov/pubmed/30108593
http://dx.doi.org/10.3389/fimmu.2018.01783
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