Risk stratification and pathway analysis based on graph neural network and interpretable algorithm
Abstract Background Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-09-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04950-1 |
| _version_ | 1811253764175167488 |
|---|---|
| author | Bilin Liang Haifan Gong Lu Lu Jie Xu |
| author_facet | Bilin Liang Haifan Gong Lu Lu Jie Xu |
| author_sort | Bilin Liang |
| collection | DOAJ |
| description | Abstract Background Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result. Results To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival. Conclusion PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power. |
| first_indexed | 2024-04-12T16:56:06Z |
| format | Article |
| id | doaj.art-d4077e7674c0482e9cebcb858ba4e189 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T16:56:06Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d4077e7674c0482e9cebcb858ba4e1892022-12-22T03:24:14ZengBMCBMC Bioinformatics1471-21052022-09-0123111310.1186/s12859-022-04950-1Risk stratification and pathway analysis based on graph neural network and interpretable algorithmBilin Liang0Haifan Gong1Lu Lu2Jie Xu3Shanghai Artificial Intelligence LaboratoryShanghai Artificial Intelligence LaboratoryShanghai Artificial Intelligence LaboratoryShanghai Artificial Intelligence LaboratoryAbstract Background Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result. Results To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival. Conclusion PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.https://doi.org/10.1186/s12859-022-04950-1Graph neural networkDeep learningRisk classificationPathwayInterpretability |
| spellingShingle | Bilin Liang Haifan Gong Lu Lu Jie Xu Risk stratification and pathway analysis based on graph neural network and interpretable algorithm BMC Bioinformatics Graph neural network Deep learning Risk classification Pathway Interpretability |
| title | Risk stratification and pathway analysis based on graph neural network and interpretable algorithm |
| title_full | Risk stratification and pathway analysis based on graph neural network and interpretable algorithm |
| title_fullStr | Risk stratification and pathway analysis based on graph neural network and interpretable algorithm |
| title_full_unstemmed | Risk stratification and pathway analysis based on graph neural network and interpretable algorithm |
| title_short | Risk stratification and pathway analysis based on graph neural network and interpretable algorithm |
| title_sort | risk stratification and pathway analysis based on graph neural network and interpretable algorithm |
| topic | Graph neural network Deep learning Risk classification Pathway Interpretability |
| url | https://doi.org/10.1186/s12859-022-04950-1 |
| work_keys_str_mv | AT bilinliang riskstratificationandpathwayanalysisbasedongraphneuralnetworkandinterpretablealgorithm AT haifangong riskstratificationandpathwayanalysisbasedongraphneuralnetworkandinterpretablealgorithm AT lulu riskstratificationandpathwayanalysisbasedongraphneuralnetworkandinterpretablealgorithm AT jiexu riskstratificationandpathwayanalysisbasedongraphneuralnetworkandinterpretablealgorithm |