A Local-Global Multi-View Diffusion Variational Graph Auto-Encoder for lncRNA-Protein Interaction Prediction

Long non-coding RNAs (lncRNAs) interact with proteins, influencing cell growth, differentiation, and disease onset. Despite significant advancements in computational methods, current approaches rely heavily on manually engineered features and require improved feature fusion techniques. Furthermore, prior studies have predominantly utilized supervised and semi-supervised learning techniques, which fail to effectively harness limited data from various sources, significantly constraining their generalizability and performance across diverse scenarios. Additionally, existing variational graph auto-encoders (VGAE) do not adequately capture long-range interactions of biomolecules. Therefore, this study introduces the Local-Global Multi-View Diffusion Variational Graph Auto-encoder (LG-MDVGA) for predicting lncRNA-protein interactions (LPIs). LG-MDVGA integrates a feature construction and fusion module that creates parameterized feature matrices for lncRNAs and proteins, which are updated through backpropagation. To capture local features effectively, separate adaptive local multi-modal feature matrices for lncRNAs and proteins are constructed. To fully utilize limited data to capture global data features and enhance predictive accuracy and generalization, LG-MDVGA incorporates a global multi-space collaborative computation by self-supervised learning. In addition, it introduces a diffusion variational graph auto-encoder (DVGA) to address the limitation that traditional VGAE have difficulty in capturing the complex patterns and relationships of LPIs. Experimental results show that LG-MDVGA significantly outperforms current methods and holds potential for discovering new LPIs. Additionally, LG-MDVGA was tested on five datasets involving three other types of biological entities and consistently attained superior performance. This underscores the generalizability and high precision of LG-MDVGA in accurately predicting associations among biological entities.