GIN-CRC-Pareto: A graph-based pareto-optimized multi-task learning framework to identify miRNA-target interactions in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) ranks as the third highest incidence among malignancies for human and the second most common cause of cancer-related mortality in the United States. Accumulating evidence has established microRNAs (miRNAs) as critical regulators of cancer development and therapeutic response. Understanding miRNA-mRNA interactions is critical for elucidating the molecular mechanisms driving CRC and other malignancies. However, accurately modeling miRNA-mRNA interactions and their binding patterns remains challenging. METHODS: In this study, we proposed GIN-CRC-Pareto, a graph-based, Pareto-optimized multi-task learning framework that simultaneously predicts miRNA-mRNA binding pairs, identifies seed match pairings, and classifies seed match subtypes. By leveraging the power of graph neural networks and Pareto-optimized gradient balancing strategy, GIN-CRC-Pareto dynamically adjusted the task weights during training to optimize each task without compromising the others. RESULTS: Experimental results demonstrated that our framework consistently outperforms traditional deep learning models and existing state-of-the-art tools across multiple evaluation metrics, with 0.909 in accuracy, 0.909 in precision and 0.969 in AUC in the miRNA-mRNA binding pairs prediction task. Furthermore, transfer learning experiments on external datasets indicate strong generalizability of the framework for identifying miRNA-target interactions across multiple cancer types. CONCLUSIONS: The proposed framework provides an effective and scalable approach for comprehensive identification of miRNA-target interactions in CRC, with the potential to serve as a scalable and generalizable tool across diverse cancer types, ultimately facilitating the development of miRNA-based therapeutics for cancer treatment.