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MambaCAttnGCN+:一个全面的框架,整合了MambaTextCNN,交叉注意力和图形卷积网络,用于piRNA-疾病关联预测

Dengju Yao¹, Xiangkui Li², Xiaojuan Zhan³

¹School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China. ydkvictory@hrbust.edu.c.

Scientific reports

|July 11, 2025

概括

这项研究介绍了MambaCAttnGCN+,这是一种用于预测piRNA-疾病关联的新型计算模型. 它准确地识别了piwi相互作用RNAs (piRNAs) 和疾病之间的联系,有助于生物医学研究.

科学领域:

生物医学信息学是生物医学信息学.
计算生物学是一种计算生物学.
基因组学就是基因组学.

背景情况:

了解piwi相互作用RNA (piRNA) 和疾病相互作用对于诊断和治疗至关重要.
现有的计算方法在稀疏的数据上扎,限制了准确的piRNA-疾病关联预测.

研究的目的:

开发一种更准确的计算模型来预测piRNA与疾病的关联.
利用异质图形构造和先进的序列嵌入来改善特征提取.

主要方法:

构建了一个整合piRNA序列,疾病语义和已知的关联的异质图.
使用MambaTextCNN用于piRNA序列特征提取和异质图形卷曲与交叉注意.
利用积极的未标记学习来开发MambaCAttnGCN+预测模型.

主要成果:

在两个数据集的5倍交叉验证中,MambaCAttnGCN+实现了0.94和0.953的高AUC.
该模型在预测piRNA-疾病关联方面表现优于其他七种最先进的方法.
除研究证实了MambaTextCNN在提取序列节点特征方面的卓越性能.

结论:

MambaCAttnGCN+显示出作为对piRNA-疾病关联的预测工具的巨大潜力.
这些发现强调了整合序列信息,图形网络和高级学习技术的有效性.
这种方法有助于研究piRNA在各种疾病中的作用.

关键词:

交叉注意力交叉注意力交叉注意力马姆巴·马姆巴是什么意思这就是PiRNA.皮RNA与疾病的关联积极的未标记的学习.

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piRNA - Piwi-interacting RNAs

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