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Published on: July 22, 2020
Multiplex networks-based directed graph neural network for cancer driver gene identification
1College of Information Science and Engineering, Hunan Normal University, Changsha, China.
Abstract:
Identifying cancer driver genes is crucial in precision oncology. Most existing methods rely on a single interaction network to capture gene relationships. However, with the increasing availability of multi-omics and biological network data, integrating multiplex networks offers a more comprehensive representation of the complex and directional regulatory interactions among genes. Moreover, the number of validated cancer driver genes remains small compared with the vast number of unlabeled genes, leading to label scarcity and class imbalance. To address these limitations, we propose a multiplex networks-based directed graph neural network (MNDGNN). The model learns gene representations on multiplex networks with multi-omics data through directed graph convolution, which integrates neighbor diversity and degree diversity. We also incorporate data augmentation combining positive-sample augmentation with negative-sample inference to mitigate label scarcity. Experimental results show that the proposed method achieves better predictive performance and robustness than existing state-of-the-art methods. The predicted cancer driver genes are significantly enriched in cancer-related pathways and exhibit extensive interactions with known cancer driver genes, offering a new perspective for cancer driver gene discovery and the design of therapeutic strategies.
Insights
This study introduces a novel multiplex network-based directed graph neural network (MNDGNN) to identify cancer driver genes. The method improves prediction accuracy by integrating multi-omics data and addressing data imbalance for better precision oncology insights.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Identifying cancer driver genes is vital for precision oncology.
- Current methods often use single networks, limiting comprehensive gene relationship analysis.
- Integrating multiplex networks with multi-omics data offers a richer view of gene interactions.
Purpose of the Study:
- To develop a novel method for identifying cancer driver genes.
- To address limitations of existing methods, including label scarcity and class imbalance.
- To leverage multiplex networks and multi-omics data for improved gene representation.
Main Methods:
- Proposed a multiplex networks-based directed graph neural network (MNDGNN).
- Utilized directed graph convolution to integrate neighbor and degree diversity from multiplex networks.
- Incorporated data augmentation (positive-sample augmentation and negative-sample inference) to mitigate label scarcity.
Main Results:
- MNDGNN demonstrated superior predictive performance and robustness compared to state-of-the-art methods.
- Predicted cancer driver genes were significantly enriched in cancer-related pathways.
- Identified extensive interactions between predicted genes and known cancer drivers.
Conclusions:
- The MNDGNN model effectively identifies cancer driver genes by integrating multiplex networks and multi-omics data.
- The approach offers a new perspective for cancer driver gene discovery.
- Findings support the design of novel therapeutic strategies in precision oncology.
