Integrating Multi-Modal Biological Knowledge via Contrastive Dual-View Graph Learning for Phosphorylation...

Accurately characterizing the associations between phosphorylation sites (psites) and diseases is essential for elucidating pathogenic mechanisms and guiding therapeutic discovery. However, existing computational approaches for phosphorylation site-disease association (PDA) prediction remain limited, as they often overlook the integration of biological knowledge across multiple modalities.

We propose CDVGL-PDA, a contrastive dual-view graph learning framework for PDA prediction. Specifically, we construct a multi-modal heterogeneous graph encompassing nine node types and ten edge types, enabling comprehensive representation of phosphorylation-centric biological networks. For phosphorylation site nodes, CDVGL-PDA incorporates sequence-derived embeddings, while disease nodes are initialized with semantic features from BioBERT. The framework then performs dual-view heterogeneous graph encoding, aligns representations through contrastive learning, and adaptively integrates them via attention-based fusion to capture informative embeddings. Extensive evaluations demonstrate CDVGL-PDA's strong predictive performance across balanced, imbalanced, and low-similarity datasets. Analysis of embeddings reveals that the model effectively captures latent biological relationships. Ablation and visualization studies validate the contributions of each module, while case studies highlight its ability to uncover potential PDAs, illustrating its promise for advancing disease mechanism research and therapeutic target discovery.

The source code and datasets for CDVGL-PDA are publicly available in the GitHub repository at https://github.com/ljquanlab/CDVGL-PDA/.

Supplementary data are available at Bioinformatics online.