MWMF-GLRW: Using Smart Model to Accurately Predict Non-Coding RNA Interactions for Healthy Consumption

In the rapidly evolving field of consumer healthcare, the exploration of non-coding RNA interactions is crucial for drug development and personalized therapy. However, through traditional experimental validation methods, it is usually costly in terms of labor and money. In this article, we propose a efficient smart model utilizing multi-perspective weighted matrix factorization with global and local interactive-based random walk (MWMF-GLRW) to assist personalized treatment. First, only known interaction information was used to compute noncoding RNA similarities and perform fusions to ensure simplicity and generalizability of the model. Second, we innovatively develop a multi-perspective weighted matrix factorization technique. This method extracts key features while preserving the matrix structure, effectively enhancing the robustness and formative nature of miRNA-lncRNA edges between miRNA and lncRNA networks. Third, we introduce a new random walk method that considers both global information and local details of heterogeneous networks. This iterative interaction mechanism dynamically adjusts the model, enhancing its robustness and accuracy. Experiments show that MWMF-GLRW surpasses the state-of-the-art model on three datasets using only known interaction information. Notably, the simplicity of our methodology, combined with its high predictive efficiency, makes it well-suited for application in medical electronic devices aimed at promoting healthy patient consumption.