APDCA: An accurate and effective method for predicting associations between RBPs and AS-events during epithelial-mesenchymal transition

Motivation: Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progression. Disruption of RBP-AS interactions may disrupt the progress of diseases like cancer. Despite the importance of RBP-AS relationships in EMT, few computational methods predict these associations. Existing models struggle in sparse settings with limited known associations. To improve performance, we incorporate both sparsity constraints and heterogeneous biological data to infer RBP–AS associations.Result: We propose a new method based on Accelerated Proximal DC Algorithm (APDCA) for predicting RBP–AS associations. In particular, APDCA combines sparse low-rank matrix factorization with a Difference-of-Convex (DC) optimization framework and uses extrapolation to improve convergence. A key feature of APDCA is the use of a sparsity constraint, which filters out noise and highlights key associations. In addition, integrating multiple related data sources with direct or indirect relationships can help in reaching a more comprehensive view of RBPs and AS events and to reduce the impact of false positives associated with individual data sources. we prove that our proposed algorithm is convergent under some conditions and the experimental results have illustrated that APDCA outperforms six baseline methods in both AUC and AUPR. A case study on the RBP QKI shows that the top predictions are verified by the OncoSplicing database. Thus, APDCA provides a fast, interpretable, and scalable tool for discovering post-transcriptional regulatory interactions.Author summary: Epithelial–mesenchymal transition (EMT) is a fundamental biological process closely linked to cancer metastasis, where RNA-binding proteins (RBPs) play a critical role by regulating alternative splicing (AS) events. Accurately identifying RBP–AS associations during EMT is essential for understanding post-transcriptional regulation, yet existing computational approaches often struggle with the sparsity of known interactions and the heterogeneity of biological data. To address these challenges, we propose APDCA, an Accelerated Proximal Difference-of-Convex Algorithm, which integrates heterogeneous biological data and formulates the prediction task as a difference-of-convex optimisation problem. APDCA introduces a proximal optimisation scheme enhanced by Nesterov extrapolation, enabling both noise suppression and efficient convergence. The algorithm jointly enforces low-rank structure to capture shared regulatory patterns and sparsity constraints to filter out spurious associations. Moreover, it incorporates adaptive weight updating and error controlled regularisation, eliminating the need for pre-constructed similarity graphs or manual parameter tuning. This unified framework enables APDCA to learn biologically meaningful and interpretable associations with high computational efficiency. Through extensive experiments, APDCA demonstrates superior performance over six existing methods and rapidly identifies high-confidence RBP–AS pairs for downstream biological validation.