MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data
Gene regulatory networks (GRNs) describe the interactions between transcription factors (TFs) and their target genes, playing a crucial role in understanding gene functions and how cells regulate gene expression under different conditions. Recent advancements in multi-omics technologies have provide...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/13/5/124 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850257916521611264 |
|---|---|
| author | Dongbo Liu Hao Chen Jianxin Wang Yeru Wang |
| author_facet | Dongbo Liu Hao Chen Jianxin Wang Yeru Wang |
| author_sort | Dongbo Liu |
| collection | DOAJ |
| description | Gene regulatory networks (GRNs) describe the interactions between transcription factors (TFs) and their target genes, playing a crucial role in understanding gene functions and how cells regulate gene expression under different conditions. Recent advancements in multi-omics technologies have provided new opportunities for more comprehensive GRN inference. Among these data types, gene expression and chromatin accessibility are particularly important, as they are key to distinguishing between direct and indirect regulatory relationships. However, existing methods primarily rely on gene expression data while neglecting biological information such as chromatin accessibility, leading to an increased occurrence of false positives in the inference results. To address the limitations of existing approaches, we propose MultiGNN, a supervised framework based on graph neural networks (GNNs). Unlike conventional GRN inference methods, MultiGNN leverages features extracted from both gene expression and chromatin accessibility data to predict regulatory interactions between genes. Experimental results demonstrate that MultiGNN consistently outperforms other methods across seven datasets. Additionally, ablation studies validate the effectiveness of our multi-omics feature integration strategy, offering a new direction for more accurate GRN inference. |
| format | Article |
| id | doaj-art-cb5ed00281024cab82855e36d47e2ab6 |
| institution | OA Journals |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-cb5ed00281024cab82855e36d47e2ab62025-08-20T01:56:17ZengMDPI AGComputation2079-31972025-05-0113512410.3390/computation13050124MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics DataDongbo Liu0Hao Chen1Jianxin Wang2Yeru Wang3School of Information, Beijing Forestry University, Beijing 100083, ChinaSchool of Information, Beijing Forestry University, Beijing 100083, ChinaSchool of Information, Beijing Forestry University, Beijing 100083, ChinaRisk Assessment Division 1, China National Center for Food Safety Risk Assessment, Beijing 100022, ChinaGene regulatory networks (GRNs) describe the interactions between transcription factors (TFs) and their target genes, playing a crucial role in understanding gene functions and how cells regulate gene expression under different conditions. Recent advancements in multi-omics technologies have provided new opportunities for more comprehensive GRN inference. Among these data types, gene expression and chromatin accessibility are particularly important, as they are key to distinguishing between direct and indirect regulatory relationships. However, existing methods primarily rely on gene expression data while neglecting biological information such as chromatin accessibility, leading to an increased occurrence of false positives in the inference results. To address the limitations of existing approaches, we propose MultiGNN, a supervised framework based on graph neural networks (GNNs). Unlike conventional GRN inference methods, MultiGNN leverages features extracted from both gene expression and chromatin accessibility data to predict regulatory interactions between genes. Experimental results demonstrate that MultiGNN consistently outperforms other methods across seven datasets. Additionally, ablation studies validate the effectiveness of our multi-omics feature integration strategy, offering a new direction for more accurate GRN inference.https://www.mdpi.com/2079-3197/13/5/124network inferencegene regulatory networkdeep learning |
| spellingShingle | Dongbo Liu Hao Chen Jianxin Wang Yeru Wang MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data Computation network inference gene regulatory network deep learning |
| title | MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data |
| title_full | MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data |
| title_fullStr | MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data |
| title_full_unstemmed | MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data |
| title_short | MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data |
| title_sort | multignn a graph neural network framework for inferring gene regulatory networks from single cell multi omics data |
| topic | network inference gene regulatory network deep learning |
| url | https://www.mdpi.com/2079-3197/13/5/124 |
| work_keys_str_mv | AT dongboliu multignnagraphneuralnetworkframeworkforinferringgeneregulatorynetworksfromsinglecellmultiomicsdata AT haochen multignnagraphneuralnetworkframeworkforinferringgeneregulatorynetworksfromsinglecellmultiomicsdata AT jianxinwang multignnagraphneuralnetworkframeworkforinferringgeneregulatorynetworksfromsinglecellmultiomicsdata AT yeruwang multignnagraphneuralnetworkframeworkforinferringgeneregulatorynetworksfromsinglecellmultiomicsdata |