A rapid prediction model for MiRNA-LncRNA interactions utilizing multi-view projection fusion and random parallel matrix factorization.

The interaction between miRNAs and lncRNAs has a crucial effect in the gene expression regulatory network, significantly influencing cellular functions and the development of diseases. Therefore, inferring potential miRNA-lncRNA interactions is essential. With the increase in data size, the computational complexity of models has grown, necessitating efficient methods for fast prediction of molecular interactions. Additionally, most existing models rely on a single similarity metric, introducing bias into computational modeling. The potential of similarity-based topologies has also not been fully explored or utilized. In this study, we propose a rapid prediction model for miRNA-lncRNA interactions utilizing Multi-view Projection Fusion and Random Parallel Matrix Factorization (MVPFDPC). First, we construct a comprehensive miRNA/lncRNA similarity profile. Second, we have developed a multi-view projection fusion technique that leverages multiple similarity network topologies from various spaces along with the original interaction matrix structure to update the interaction matrix. Third, we propose Divide-Parallel-Combine (DPC), a parallel acceleration framework utilizing truncated matrix factorization. DPC decomposes large-scale matrix tasks into smaller subproblems, solves each subproblem in parallel using truncated matrix factorization, and combines the solutions utilizing randomized matrix approximation techniques. Extensive experiments on three datasets showed that MVPFDPC is a highly accurate and fast predictive model for miRNA-lncRNA interactions.