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TRV-2026-1010Certified recordPeer-reviewed

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 pattern…

Health · G Space — documented gain · certified 2026-09-07 · v1 · article view · machine-readable

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GIN-CRC-Pareto improved identification of miRNA-mRNA interactions in colorectal cancer, achieving 0.909 accuracy and 0.969 AUC on binding pair prediction and outperforming existing tools.

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Truvace Impact Record TRV-2026-1010, v1: “GIN-CRC-Pareto: A graph-based pareto-optimized multi-task learning framework to identify miRNA-target interactions in colorectal cancer.” Truvace, 2026-09-07. /record/TRV-2026-1010 (accessed at citation time). sha256 0f42ce92ebd669b2…

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