Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
Biomedical Big Data Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Bioinformatics. 2022 Sep 15;38(18):4380-4386. doi: 10.1093/bioinformatics/btac520.
Quantifying the similarity of human diseases provides guiding insights to the discovery of micro-scope mechanisms from a macro scale. Previous work demonstrated that better performance can be gained by integrating multiview data sources or applying machine learning techniques. However, designing an efficient framework to extract and incorporate information from different biological data using deep learning models remains unexplored.
We present CoGO, a Contrastive learning framework to predict disease similarity based on Gene network and Ontology structure, which incorporates the gene interaction network and gene ontology (GO) domain knowledge using graph deep learning models. First, graph deep learning models are applied to encode the features of genes and GO terms from separate graph structure data. Next, gene and GO features are projected to a common embedding space via a nonlinear projection. Then cross-view contrastive loss is applied to maximize the agreement of corresponding gene-GO associations and lead to meaningful gene representation. Finally, CoGO infers the similarity between diseases by the cosine similarity of disease representation vectors derived from related gene embedding. In our experiments, CoGO outperforms the most competitive baseline method on both AUROC and AUPRC, especially improves 19.57% in AUPRC (0.7733). The prediction results are significantly comparable with other disease similarity studies and thus highly credible. Furthermore, we conduct a detailed case study of top similar disease pairs which is demonstrated by other studies. Empirical results show that CoGO achieves powerful performance in disease similarity problem.
量化人类疾病的相似性为从宏观尺度发现微观机制提供了指导见解。以前的工作表明，通过整合多视图数据源或应用机器学习技术可以获得更好的性能。然而，使用深度学习模型设计一种从不同生物数据中提取和整合信息的有效框架仍然是一个未被探索的问题。
我们提出了 CoGO，这是一种基于基因网络和本体结构预测疾病相似性的对比学习框架，它使用图深度学习模型整合了基因相互作用网络和基因本体 (GO) 领域知识。首先，图深度学习模型应用于从单独的图结构数据中编码基因和 GO 术语的特征。接下来，通过非线性投影将基因和 GO 特征投影到公共嵌入空间。然后应用跨视图对比损失来最大化对应基因-GO 关联的一致性，并导致有意义的基因表示。最后，CoGO 通过从相关基因嵌入中得出的疾病表示向量的余弦相似度来推断疾病之间的相似性。在我们的实验中，CoGO 在 AUROC 和 AUPRC 上均优于最具竞争力的基线方法，尤其是在 AUPRC 上提高了 19.57%（0.7733）。预测结果与其他疾病相似性研究高度可比，因此具有高度可信度。此外，我们对其他研究证明的顶级相似疾病对进行了详细的案例研究。实验结果表明，CoGO 在疾病相似性问题上具有强大的性能。
