Cargando…

GraphTar: applying word2vec and graph neural networks to miRNA target prediction

BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that regulate gene expression by binding to specific mRNAs, inhibiting their translation. They play a critical role in regulating various biological processes and are implicated in many diseases, including cardiovascular, oncological...

Descripción completa

Detalles Bibliográficos
Autores principales: Przybyszewski, Jan, Malawski, Maciej, Lichołai, Sabina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657114/
https://www.ncbi.nlm.nih.gov/pubmed/37978418
http://dx.doi.org/10.1186/s12859-023-05564-x
_version_ 1785148119409754112
author Przybyszewski, Jan
Malawski, Maciej
Lichołai, Sabina
author_facet Przybyszewski, Jan
Malawski, Maciej
Lichołai, Sabina
author_sort Przybyszewski, Jan
collection PubMed
description BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that regulate gene expression by binding to specific mRNAs, inhibiting their translation. They play a critical role in regulating various biological processes and are implicated in many diseases, including cardiovascular, oncological, gastrointestinal diseases, and viral infections. Computational methods that can identify potential miRNA–mRNA interactions from raw data use one-dimensional miRNA–mRNA duplex representations and simple sequence encoding techniques, which may limit their performance. RESULTS: We have developed GraphTar, a new target prediction method that uses a novel graph-based representation to reflect the spatial structure of the miRNA–mRNA duplex. Unlike existing approaches, we use the word2vec method to accurately encode RNA sequence information. In conjunction with the novel encoding method, we use a graph neural network classifier that can accurately predict miRNA–mRNA interactions based on graph representation learning. As part of a comparative study, we evaluate three different node embedding approaches within the GraphTar framework and compare them with other state-of-the-art target prediction methods. The results show that the proposed method achieves similar performance to the best methods in the field and outperforms them on one of the datasets. CONCLUSIONS: In this study, a novel miRNA target prediction approach called GraphTar is introduced. Results show that GraphTar is as effective as existing methods and even outperforms them in some cases, opening new avenues for further research. However, the expansion of available datasets is critical for advancing the field towards real-world applications.
format Online
Article
Text
id pubmed-10657114
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-106571142023-11-17 GraphTar: applying word2vec and graph neural networks to miRNA target prediction Przybyszewski, Jan Malawski, Maciej Lichołai, Sabina BMC Bioinformatics Research BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that regulate gene expression by binding to specific mRNAs, inhibiting their translation. They play a critical role in regulating various biological processes and are implicated in many diseases, including cardiovascular, oncological, gastrointestinal diseases, and viral infections. Computational methods that can identify potential miRNA–mRNA interactions from raw data use one-dimensional miRNA–mRNA duplex representations and simple sequence encoding techniques, which may limit their performance. RESULTS: We have developed GraphTar, a new target prediction method that uses a novel graph-based representation to reflect the spatial structure of the miRNA–mRNA duplex. Unlike existing approaches, we use the word2vec method to accurately encode RNA sequence information. In conjunction with the novel encoding method, we use a graph neural network classifier that can accurately predict miRNA–mRNA interactions based on graph representation learning. As part of a comparative study, we evaluate three different node embedding approaches within the GraphTar framework and compare them with other state-of-the-art target prediction methods. The results show that the proposed method achieves similar performance to the best methods in the field and outperforms them on one of the datasets. CONCLUSIONS: In this study, a novel miRNA target prediction approach called GraphTar is introduced. Results show that GraphTar is as effective as existing methods and even outperforms them in some cases, opening new avenues for further research. However, the expansion of available datasets is critical for advancing the field towards real-world applications. BioMed Central 2023-11-17 /pmc/articles/PMC10657114/ /pubmed/37978418 http://dx.doi.org/10.1186/s12859-023-05564-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Przybyszewski, Jan
Malawski, Maciej
Lichołai, Sabina
GraphTar: applying word2vec and graph neural networks to miRNA target prediction
title GraphTar: applying word2vec and graph neural networks to miRNA target prediction
title_full GraphTar: applying word2vec and graph neural networks to miRNA target prediction
title_fullStr GraphTar: applying word2vec and graph neural networks to miRNA target prediction
title_full_unstemmed GraphTar: applying word2vec and graph neural networks to miRNA target prediction
title_short GraphTar: applying word2vec and graph neural networks to miRNA target prediction
title_sort graphtar: applying word2vec and graph neural networks to mirna target prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657114/
https://www.ncbi.nlm.nih.gov/pubmed/37978418
http://dx.doi.org/10.1186/s12859-023-05564-x
work_keys_str_mv AT przybyszewskijan graphtarapplyingword2vecandgraphneuralnetworkstomirnatargetprediction
AT malawskimaciej graphtarapplyingword2vecandgraphneuralnetworkstomirnatargetprediction
AT lichołaisabina graphtarapplyingword2vecandgraphneuralnetworkstomirnatargetprediction