Identifying potential small molecule-miRNA associations via Robust PCA based on ?-norm regularization

Dysregulation of microRNAs (miRNAs) is closely associated with refractory human diseases, and the identification of potential associations between small molecule (SM) drugs and miRNAs can provide valuable insights for clinical treatment. Existing computational techniques for inferring potential associations suffer from limitations in terms of accuracy and efficiency. To address these challenges, we devise a novel predictive model called RPCA$\Gamma $NR, in which we propose a new Robust principal component analysis (PCA) framework based on $\gamma $-norm and $l_{2,1}$-norm regularization and design an Augmented Lagrange Multiplier method to optimize it, thereby deriving the association scores. The Gaussian Interaction Profile Kernel Similarity is calculated to capture the similarity information of SMs and miRNAs in known associations. Through extensive evaluation, including Cross Validation Experiments, Independent Validation Experiment, Efficiency Analysis, Ablation Experiment, Matrix Sparsity Analysis, and Case Studies, RPCA$\Gamma $NR outperforms state-of-the-art models concerning accuracy, efficiency and robustness. In conclusion, RPCA$\Gamma $NR can significantly streamline the process of determining SM-miRNA associations, thus contributing to advancements in drug development and disease treatment.

Automated summary

In this study, a novel predictive model called RPCA$\Gamma $NR is proposed, which utilizes a new robust PCA framework based on $\gamma $-norm and $l_{2,1}$-norm regularization and an augmented Lagrange multiplier method to optimize it, thereby deriving the association scores for SM-miRNA. Through extensive evaluation, RPCA$\Gamma $NR outperforms existing models in terms of accuracy, efficiency, and robustness, significantly streamlining the process of determining SM-miRNA associations.