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ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning

Phosphorylation exerts a crucial role in multiple biological cellular processes which is catalyzed by protein kinases and closely related to many diseases. Identification of kinase–substrate relationships is important for understanding phosphorylation and provides a fundamental basis for further dis...

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Detalles Bibliográficos
Autores principales: Wang, Minghui, Wang, Tao, Li, Ao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5741978/
https://www.ncbi.nlm.nih.gov/pubmed/29340231
http://dx.doi.org/10.7717/peerj.4182
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author Wang, Minghui
Wang, Tao
Li, Ao
author_facet Wang, Minghui
Wang, Tao
Li, Ao
author_sort Wang, Minghui
collection PubMed
description Phosphorylation exerts a crucial role in multiple biological cellular processes which is catalyzed by protein kinases and closely related to many diseases. Identification of kinase–substrate relationships is important for understanding phosphorylation and provides a fundamental basis for further disease-related research and drug design. In this study, we develop a novel computational method to identify kinase–substrate relationships based on multiple kernel learning. The comparative analysis is based on a 10-fold cross-validation process and the dataset collected from the Phospho.ELM database. The results show that ksrMKL is greatly improved in various measures when compared with the single kernel support vector machine. Furthermore, with an independent test dataset extracted from the PhosphoSitePlus database, we compare ksrMKL with two existing kinase–substrate relationship prediction tools, namely iGPS and PKIS. The experimental results show that ksrMKL has better prediction performance than these existing tools.
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spelling pubmed-57419782018-01-16 ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning Wang, Minghui Wang, Tao Li, Ao PeerJ Bioinformatics Phosphorylation exerts a crucial role in multiple biological cellular processes which is catalyzed by protein kinases and closely related to many diseases. Identification of kinase–substrate relationships is important for understanding phosphorylation and provides a fundamental basis for further disease-related research and drug design. In this study, we develop a novel computational method to identify kinase–substrate relationships based on multiple kernel learning. The comparative analysis is based on a 10-fold cross-validation process and the dataset collected from the Phospho.ELM database. The results show that ksrMKL is greatly improved in various measures when compared with the single kernel support vector machine. Furthermore, with an independent test dataset extracted from the PhosphoSitePlus database, we compare ksrMKL with two existing kinase–substrate relationship prediction tools, namely iGPS and PKIS. The experimental results show that ksrMKL has better prediction performance than these existing tools. PeerJ Inc. 2017-12-20 /pmc/articles/PMC5741978/ /pubmed/29340231 http://dx.doi.org/10.7717/peerj.4182 Text en © 2017 Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Wang, Minghui
Wang, Tao
Li, Ao
ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning
title ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning
title_full ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning
title_fullStr ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning
title_full_unstemmed ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning
title_short ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning
title_sort ksrmkl: a novel method for identification of kinase–substrate relationships using multiple kernel learning
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5741978/
https://www.ncbi.nlm.nih.gov/pubmed/29340231
http://dx.doi.org/10.7717/peerj.4182
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