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iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers

BACKGROUND: Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and...

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Autores principales: Arora, Pooja, Periwal, Neha, Goyal, Yash, Sood, Vikas, Kaur, Baljeet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088697/
https://www.ncbi.nlm.nih.gov/pubmed/37041520
http://dx.doi.org/10.1186/s12859-023-05248-6
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author Arora, Pooja
Periwal, Neha
Goyal, Yash
Sood, Vikas
Kaur, Baljeet
author_facet Arora, Pooja
Periwal, Neha
Goyal, Yash
Sood, Vikas
Kaur, Baljeet
author_sort Arora, Pooja
collection PubMed
description BACKGROUND: Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimmune diseases. Interestingly, the recent association of IL-13 with COVID-19 severity has sparked interest in this cytokine. Therefore characterization of new molecules which can regulate IL-13 induction might lead to novel therapeutics. RESULTS: Here, we present an improved prediction of IL-13-inducing peptides. The positive and negative datasets were obtained from a recent study (IL13Pred) and the Pfeature algorithm was used to compute features for the peptides. As compared to the state-of-the-art which used the regularization based feature selection technique (linear support vector classifier with the L1 penalty), we used a multivariate feature selection technique (minimum redundancy maximum relevance) to obtain non-redundant and highly relevant features. In the proposed study (improved IL-13 prediction (iIL13Pred)), the use of the mRMR feature selection method is instrumental in choosing the most discriminatory features of IL-13-inducing peptides with improved performance. We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. We report improved AUC, and MCC scores of 0.83 and 0.33 on validation data as compared to the current method. CONCLUSIONS: Extensive benchmarking experiments suggest that the proposed method (iIL13Pred) could provide improved performance metrics in terms of sensitivity, specificity, accuracy, the area under the curve - receiver operating characteristics (AUCROC) and Matthews correlation coefficient (MCC) than the existing state-of-the-art approach (IL13Pred) on the validation dataset and an external dataset comprising of experimentally validated IL-13-inducing peptides. Additionally, the experiments were performed with an increased number of experimentally validated training datasets to obtain a more robust model. A user-friendly web server (www.soodlab.com/iil13pred) is also designed to facilitate rapid screening of IL-13-inducing peptides. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05248-6.
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spelling pubmed-100886972023-04-12 iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers Arora, Pooja Periwal, Neha Goyal, Yash Sood, Vikas Kaur, Baljeet BMC Bioinformatics Research BACKGROUND: Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimmune diseases. Interestingly, the recent association of IL-13 with COVID-19 severity has sparked interest in this cytokine. Therefore characterization of new molecules which can regulate IL-13 induction might lead to novel therapeutics. RESULTS: Here, we present an improved prediction of IL-13-inducing peptides. The positive and negative datasets were obtained from a recent study (IL13Pred) and the Pfeature algorithm was used to compute features for the peptides. As compared to the state-of-the-art which used the regularization based feature selection technique (linear support vector classifier with the L1 penalty), we used a multivariate feature selection technique (minimum redundancy maximum relevance) to obtain non-redundant and highly relevant features. In the proposed study (improved IL-13 prediction (iIL13Pred)), the use of the mRMR feature selection method is instrumental in choosing the most discriminatory features of IL-13-inducing peptides with improved performance. We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. We report improved AUC, and MCC scores of 0.83 and 0.33 on validation data as compared to the current method. CONCLUSIONS: Extensive benchmarking experiments suggest that the proposed method (iIL13Pred) could provide improved performance metrics in terms of sensitivity, specificity, accuracy, the area under the curve - receiver operating characteristics (AUCROC) and Matthews correlation coefficient (MCC) than the existing state-of-the-art approach (IL13Pred) on the validation dataset and an external dataset comprising of experimentally validated IL-13-inducing peptides. Additionally, the experiments were performed with an increased number of experimentally validated training datasets to obtain a more robust model. A user-friendly web server (www.soodlab.com/iil13pred) is also designed to facilitate rapid screening of IL-13-inducing peptides. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05248-6. BioMed Central 2023-04-11 /pmc/articles/PMC10088697/ /pubmed/37041520 http://dx.doi.org/10.1186/s12859-023-05248-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Arora, Pooja
Periwal, Neha
Goyal, Yash
Sood, Vikas
Kaur, Baljeet
iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers
title iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers
title_full iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers
title_fullStr iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers
title_full_unstemmed iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers
title_short iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers
title_sort iil13pred: improved prediction of il-13 inducing peptides using popular machine learning classifiers
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088697/
https://www.ncbi.nlm.nih.gov/pubmed/37041520
http://dx.doi.org/10.1186/s12859-023-05248-6
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