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XGDAG: explainable gene–disease associations via graph neural networks

MOTIVATION: Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene–disease associations; these methods ra...

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Autores principales: Mastropietro, Andrea, De Carlo, Gianluca, Anagnostopoulos, Aris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10421968/
https://www.ncbi.nlm.nih.gov/pubmed/37531293
http://dx.doi.org/10.1093/bioinformatics/btad482
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author Mastropietro, Andrea
De Carlo, Gianluca
Anagnostopoulos, Aris
author_facet Mastropietro, Andrea
De Carlo, Gianluca
Anagnostopoulos, Aris
author_sort Mastropietro, Andrea
collection PubMed
description MOTIVATION: Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene–disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability. RESULTS: We propose a new methodology for disease gene discovery, which leverages graph-structured data using graph neural networks (GNNs) along with an explainability phase for determining the ranking of candidate genes and understanding the model’s output. Our approach is based on a positive–unlabeled learning strategy, which outperforms existing gene discovery methods by exploiting GNNs in a non-black-box fashion. Our methodology is effective even in scenarios where a large number of associated genes need to be retrieved, in which gene prioritization methods often tend to lose their reliability. AVAILABILITY AND IMPLEMENTATION: The source code of XGDAG is available on GitHub at: https://github.com/GiDeCarlo/XGDAG. The data underlying this article are available at: https://www.disgenet.org/, https://thebiogrid.org/, https://doi.org/10.1371/journal.pcbi.1004120.s003, and https://doi.org/10.1371/journal.pcbi.1004120.s004.
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spelling pubmed-104219682023-08-13 XGDAG: explainable gene–disease associations via graph neural networks Mastropietro, Andrea De Carlo, Gianluca Anagnostopoulos, Aris Bioinformatics Original Paper MOTIVATION: Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene–disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability. RESULTS: We propose a new methodology for disease gene discovery, which leverages graph-structured data using graph neural networks (GNNs) along with an explainability phase for determining the ranking of candidate genes and understanding the model’s output. Our approach is based on a positive–unlabeled learning strategy, which outperforms existing gene discovery methods by exploiting GNNs in a non-black-box fashion. Our methodology is effective even in scenarios where a large number of associated genes need to be retrieved, in which gene prioritization methods often tend to lose their reliability. AVAILABILITY AND IMPLEMENTATION: The source code of XGDAG is available on GitHub at: https://github.com/GiDeCarlo/XGDAG. The data underlying this article are available at: https://www.disgenet.org/, https://thebiogrid.org/, https://doi.org/10.1371/journal.pcbi.1004120.s003, and https://doi.org/10.1371/journal.pcbi.1004120.s004. Oxford University Press 2023-08-02 /pmc/articles/PMC10421968/ /pubmed/37531293 http://dx.doi.org/10.1093/bioinformatics/btad482 Text en © The Author(s) 2023. Published by Oxford University Press. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Mastropietro, Andrea
De Carlo, Gianluca
Anagnostopoulos, Aris
XGDAG: explainable gene–disease associations via graph neural networks
title XGDAG: explainable gene–disease associations via graph neural networks
title_full XGDAG: explainable gene–disease associations via graph neural networks
title_fullStr XGDAG: explainable gene–disease associations via graph neural networks
title_full_unstemmed XGDAG: explainable gene–disease associations via graph neural networks
title_short XGDAG: explainable gene–disease associations via graph neural networks
title_sort xgdag: explainable gene–disease associations via graph neural networks
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10421968/
https://www.ncbi.nlm.nih.gov/pubmed/37531293
http://dx.doi.org/10.1093/bioinformatics/btad482
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