MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
This work addresses the problem of discovering functionally similar genes across diverse biomedical contexts for researchers in genomics and bioinformatics, representing a novel method for a known bottleneck.
The authors tackled the challenge of learning unified gene representations from heterogeneous multimodal biological data by introducing MuSe-GNN, a model combining multimodal machine learning and graph neural networks, which outperformed state-of-the-art methods by up to 97.5% in capturing gene function similarity.
Discovering genes with similar functions across diverse biomedical contexts poses a significant challenge in gene representation learning due to data heterogeneity. In this study, we resolve this problem by introducing a novel model called Multimodal Similarity Learning Graph Neural Network, which combines Multimodal Machine Learning and Deep Graph Neural Networks to learn gene representations from single-cell sequencing and spatial transcriptomic data. Leveraging 82 training datasets from 10 tissues, three sequencing techniques, and three species, we create informative graph structures for model training and gene representations generation, while incorporating regularization with weighted similarity learning and contrastive learning to learn cross-data gene-gene relationships. This novel design ensures that we can offer gene representations containing functional similarity across different contexts in a joint space. Comprehensive benchmarking analysis shows our model's capacity to effectively capture gene function similarity across multiple modalities, outperforming state-of-the-art methods in gene representation learning by up to 97.5%. Moreover, we employ bioinformatics tools in conjunction with gene representations to uncover pathway enrichment, regulation causal networks, and functions of disease-associated or dosage-sensitive genes. Therefore, our model efficiently produces unified gene representations for the analysis of gene functions, tissue functions, diseases, and species evolution.