GAM-MDR: probing miRNA–drug resistance using a graph autoencoder based on random path masking

MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal...

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Vydané v:	Briefings in functional genomics Ročník 23; číslo 4; s. 475 - 483
Hlavní autori:	Zhou, Zhecheng, Du, Zhenya, Jiang, Xin, Zhuo, Linlin, Xu, Yixin, Fu, Xiangzheng, Liu, Mingzhe, Zou, Quan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England 19.07.2024
Predmet:	Algorithms Computational Biology - methods Drug Resistance - genetics Drug Resistance, Neoplasm - genetics Humans MicroRNAs - genetics accurate node representations miRNA–drug resistance (MDR) random path masking graph autoencoder (GAE)
ISSN:	2041-2649, 2041-2657, 2041-2657
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Abstract	MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA–drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA–drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub:https://github.com/ZZCrazy00/GAM-MDR.
AbstractList	MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA-drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA-drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub:https://github.com/ZZCrazy00/GAM-MDR.MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA-drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA-drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub:https://github.com/ZZCrazy00/GAM-MDR. MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA–drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA–drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub:https://github.com/ZZCrazy00/GAM-MDR.
Author	Jiang, Xin Du, Zhenya Zhou, Zhecheng Xu, Yixin Liu, Mingzhe Fu, Xiangzheng Zhuo, Linlin Zou, Quan
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Cites_doi	10.1016/S1672-0229(08)60044-3 10.1007/s00018-015-1922-2 10.1016/j.drup.2010.02.001 10.1093/bib/bbad247 10.1371/journal.pcbi.1007872 10.1109/TCBB.2017.2723394 10.1093/bib/bbac338 10.3390/biology12010041 10.1007/978-3-030-60802-6_25 10.1145/3580305.3599546 10.1083/jcb.201208082 10.1186/s12967-019-2009-x 10.1016/S0960-9822(98)70103-4 10.1093/bioinformatics/btt677 10.12659/MSM.901191 10.1007/s40139-012-0004-5 10.1038/s41598-017-15716-8 10.1093/bioinformatics/btz621 10.1093/bib/bbz012 10.1038/nrg2934 10.1002/humu.21641 10.3389/fgene.2014.00023 10.1101/gr.6.10.986 10.1109/ACCESS.2021.3063885
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SubjectTerms	Algorithms Computational Biology - methods Drug Resistance - genetics Drug Resistance, Neoplasm - genetics Humans MicroRNAs - genetics
Title	GAM-MDR: probing miRNA–drug resistance using a graph autoencoder based on random path masking
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