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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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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. |
format | Online Article Text |
id | pubmed-10421968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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