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

ABSTRACT: Colorectal cancer (CRC) ranks as the third highest incidence among malignancies in humans 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. In this study, we proposed GIN-CRC-Pareto, a graph-based, Pareto-optimal 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-optimal gradient balancing strategy, GIN-CRC-Pareto dynamically adjusted the task weig

SUBMITTER: Li L 

PROVIDER: S-EPMC12363797 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature