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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-8929166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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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