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GIN-CRC-Pareto:一个基于图形的Pareto-optimal多任务学习框架,用于识别结直肠癌中的miRNA-target相互作用

Lin Li¹, Qiang Yang¹, Lu Li²

¹Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, USA.

bioRxiv : the preprint server for biology

|August 20, 2025

概括

基于图形的新型框架GIN-CRC-Pareto准确地预测了对结直肠癌 (CRC) 研究至关重要的微RNA-mRNA相互作用. 该工具增强了对癌症机制的理解,并有助于开发基于miRNA的新疗法.

科学领域:

计算生物学是一种计算生物学.
基因组学就是基因组学.
在瘤学瘤学.

背景情况:

结肠直肠癌 (CRC) 是美国癌症死亡的主要原因.
微RNAs (miRNAs) 是癌症发展和对治疗的反应的关键调节者.
了解miRNA-mRNA相互作用对于破译CRC分子机制至关重要.

研究的目的:

开发一种新的计算框架,GIN-CRC-Pareto,用于预测miRNA-mRNA结合对.
同时识别种子匹配配对和分类种子匹配亚型.
在CRC中提高miRNA-目标相互作用识别的准确性和通用性.

主要方法:

使用基于图形的帕雷托最佳多任务学习框架 (GIN-CRC-Pareto).
采用图形神经网络和帕雷托最佳梯度平衡策略来进行动态任务重量调整.
使用转移学习验证了框架的概括性,用于跨癌症miRNA-目标相互作用.

主要成果:

GIN-CRC-Pareto的表现优于传统的深度学习模型和现有工具.
实现了高性能指标:0.909准确度,0.909精度和0.968 miRNA-mRNA结合对预测的AUC.
在不同癌症类型中证明了有效性和通用性.

结论:

GIN-CRC-Pareto是一种有效的工具,用于在CRC中全面识别miRNA-目标相互作用.
该框架显示了作为各种癌症类型的可扩展和可泛化的资源的潜力.
这些发现有助于开发基于miRNA的新型癌症治疗疗法.

关键词:

结肠直肠癌结肠直肠癌是什么图形神经网络的神经网络多任务处理能力.目标预测目标预测转移学习转移学习这就是miRNA-mRNA.

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相关概念视频

MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...