Integrating Multi-Modal Biological Knowledge via Contrastive Dual-View Graph Learning for Phosphorylation Site-Disease Association Prediction

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ID: 329045
2026
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Abstract
Abstract Motivation 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. Results 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. Availability and Implementation The source code and datasets for CDVGL-PDA are publicly available in the GitHub repository at https://github.com/ljquanlab/CDVGL-PDA/. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7213533665 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xiangyu Chen, Lijun Quan, Yexuan Mao, Siyuan Wang, Guozheng Zhang, Siqi Li, Yelu Jiang, Liangpeng Nie, Tingfang Wu, Qiang Lyu
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag690
URL
https://doi.org/10.1093/bioinformatics/btag690
Keywords Keywords not found

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