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.

在这项研究中，我们通过引入一种名为多模态相似性学习图神经网络的新型模型，结合多模态机器学习和深度图神经网络，从单细胞测序和空间转录组学数据中学习基因表示，以解决在不同生物医学环境中发现具有类似功能的基因的挑战。我们的模型通过为模型训练和基因表示生成创建信息丰富的图结构，并结合加权相似性学习和对比学习的正则化，学习跨数据的基因 - 基因关系，从而确保我们能够提供包含功能相似性的基因表示。广泛的基准测试分析显示，我们的模型能够有效地捕捉多种模态下的基因功能相似性，其基因表示学习的性能超过当前最先进方法高达 97.5％。此外，我们将生物信息学工具与基因表示结合使用，揭示通路富集、调控因果网络和与疾病相关或剂量敏感的基因功能。因此，我们的模型能够高效产生用于分析基因功能、组织功能、疾病和物种演化的统一基因表示。