MLWNNR: LncRNA-Disease Association Prediction with Multi-Kernel Learning-Driven Weighted Nuclear Norm Regularization

Emerging evidence highlights long non-coding RNAs (lncRNAs) as pivotal regulators demonstrating significant linkages with diverse human pathologies through expression dynamics and regulatory cascades. This research endeavors to establish an algorithm for forecasting the associations between lncRNAs and diseases based on multi-kernel learning-driven weighted nuclear norm regularization (MLWNNR). Specifically, our framework first uses a kernel learning algorithm centered on k-nearest neighbors to integrate multi-similarity kernels. Then, we construct a heterogeneous lncRNA-disease associations network utilizing similarity information and confirm lncRNA-disease associations. Finally, we adopt weighted nuclear norm regularization to complete the heterogeneous network to derive the final association prediction score. MLWNNR achieves impressive performance on three datasets and outperforms six representative models in the comparative experiments, which demonstrates its robustness and excellent generalization abilities. Furthermore, in case studies centered on three common human diseases, the majority of the hypothesized connections are corroborated by experimental literature. MLWNNR is a reliable approach for inferring lncRNA-disease associations, according to the experimental results.