Graph-based representation for specificity and heterogeneity in biomedical multi-view learning

Biomedical multi-view learning (BioMVL) has shown promise for disease classification by integrating complementary information across views. However, existing methods often overlook the specificity of each view and the semantic heterogeneity across multi-view data, limiting representation quality and interpretability. To address these problems, this paper presents a novel specificity-heterogeneity BioMVL framework (SH-BioMVL). The framework employs a global-local graph neural network (GLGNN) to disentangle the interactivity and specificity information in multi-view data. Additionally, the LD-based individual genetic graph construction method (LDIG) transforms high-dimensional genetic sequences into biologically informed representations to facilitate heterogeneous fusion. Experimental validation across different cohorts, modalities, and diseases demonstrates that SH-BioMVL achieves state-of-the-art performance and maintains robust generalization in biomedical multi-view disease classification tasks. Analysis of cross-view interactivity and specificity features further confirms the biological validity and reliability. Overall, SH-BioMVL provides an effective framework for addressing specificity and heterogeneity in BioMVL, offering strong support for solving biomedical problems.