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PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information

Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, severa...

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Detalles Bibliográficos
Autores principales: Liu, Xia, Wang, Minghui, Li, Ao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929166/
https://www.ncbi.nlm.nih.gov/pubmed/35310161
http://dx.doi.org/10.7717/peerj.12847
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author Liu, Xia
Wang, Minghui
Li, Ao
author_facet Liu, Xia
Wang, Minghui
Li, Ao
author_sort Liu, Xia
collection PubMed
description Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, several computational methods for predicting phospho-variants have been developed, but their performance still leaves considerable room for improvement. In recent years, deep learning has been successfully applied in biological sequence analysis with its efficient sequence pattern learning ability, which provides a powerful tool for improving phospho-variant prediction based on protein sequence information. In the study, we present PhosVarDeep, a novel unified deep-learning framework for phospho-variant prediction. PhosVarDeep takes reference and variant sequences as inputs and adopts a Siamese-like CNN architecture containing two identical subnetworks and a prediction module. In each subnetwork, general phosphorylation sequence features are extracted by a pre-trained sequence feature encoding network and then fed into a CNN module for capturing variant-aware phosphorylation sequence features. After that, a prediction module is introduced to integrate the outputs of the two subnetworks and generate the prediction results of phospho-variants. Comprehensive experimental results on phospho-variant data demonstrates that our method significantly improves the prediction performance of phospho-variants and compares favorably with existing conventional machine learning methods.
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spelling pubmed-89291662022-03-18 PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information Liu, Xia Wang, Minghui Li, Ao PeerJ Bioinformatics Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, several computational methods for predicting phospho-variants have been developed, but their performance still leaves considerable room for improvement. In recent years, deep learning has been successfully applied in biological sequence analysis with its efficient sequence pattern learning ability, which provides a powerful tool for improving phospho-variant prediction based on protein sequence information. In the study, we present PhosVarDeep, a novel unified deep-learning framework for phospho-variant prediction. PhosVarDeep takes reference and variant sequences as inputs and adopts a Siamese-like CNN architecture containing two identical subnetworks and a prediction module. In each subnetwork, general phosphorylation sequence features are extracted by a pre-trained sequence feature encoding network and then fed into a CNN module for capturing variant-aware phosphorylation sequence features. After that, a prediction module is introduced to integrate the outputs of the two subnetworks and generate the prediction results of phospho-variants. Comprehensive experimental results on phospho-variant data demonstrates that our method significantly improves the prediction performance of phospho-variants and compares favorably with existing conventional machine learning methods. PeerJ Inc. 2022-03-14 /pmc/articles/PMC8929166/ /pubmed/35310161 http://dx.doi.org/10.7717/peerj.12847 Text en ©2022 Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Liu, Xia
Wang, Minghui
Li, Ao
PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information
title PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information
title_full PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information
title_fullStr PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information
title_full_unstemmed PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information
title_short PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information
title_sort phosvardeep: deep-learning based prediction of phospho-variants using sequence information
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929166/
https://www.ncbi.nlm.nih.gov/pubmed/35310161
http://dx.doi.org/10.7717/peerj.12847
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