DAISY: Dual-Granularity Contrastive Learning for Disease Similarity Prediction

The quantification of similarities among human diseases is crucial for enhancing our understanding of disease biology, which can aid in improving disease diagnosis, prognosis and treatment, and drug development. Recently, efforts have been devoted to quantifying disease similarity by integrating multi-view data sources from disparate biological data. However, disease data are often sparse due to their rarity or privacy, leading to suboptimal representation of disease when biological entity relationships and labeled disease data are not adequately modeled. To address this challenge, we propose DAISY (Dual-grAnularity contrastIve learning for diSease similaritY prediction), an effective deep learning model for disease representation modeling. DAISY leverages a novel dual-granularity contrastive learning mechanism to enhance the representation of diverse biological entities. Its ability to enable the collaborative supervision of diseases represented by both homogeneous and heterogeneous information is of great significance, leading to a high level of disease representation learning. Besides, this contrastive learning mechanism combines two independent and complementary components: a hierarchical biological entity relationship-aware module to capture disease features on other biological entities, and a disease association capturing module based on signed random walk with precious disease data. Experimental results demonstrate that DAISY achieves outstanding performance on the disease similarity prediction problem.