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通过基于区块的双重自适应深度调整赋能图形神经网络,用于与药物耐药性相关的ncRNA发现

Yi Zhang^1,2, Xuanzhao Li^1,2

¹Guilin University of Technology, Guilin 541004, China.

Journal of chemical information and modeling

|March 25, 2024

概括

本研究介绍了B-NDRA,这是一个计算框架,通过整合相似性信息,准确预测非编码RNA (ncRNA) -耐药性关联 (NDRA). B-NDRA通过识别潜在的ncRNA目标来克服抗药性来增强癌症药物发现.

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.
癌症研究癌症研究

背景情况:

对化疗的耐药性是癌症治疗的一个主要障碍.
非编码RNAs (ncRNAs) 在调解癌症抗药性方面发挥着重要作用.
鉴定ncRNA药物耐药性关联的传统实验方法是耗时和劳动密集的.

研究的目的:

开发一个高效的计算框架,B-NDRA,用于预测ncRNA-药物耐药性协会 (NDRA).
通过结合ncRNAs和耐药性之间的相似性信息来克服现有模型的局限性.
为了促进发现新的ncRNA目标来克服癌症药物耐药性的发现.

主要方法:

构建了一个整合已知的ncRNA-药物耐药性对和相似性融合信息的异质图.
采用了注意力机制,用于局部特征聚合和缩小维度.
利用图形神经网络 (GNN) 来学习全球节点嵌入.
集成的双自适应深度调整架构用于特征提取和平衡.
应用多层感知子用于最终的NDRA预测.

主要成果:

在5倍交叉验证中,B-NDRA实现了高性能,平均AUC为92.2%和AUPR为91.9%.
对比评估显示,B-NDRA在多个指标上表现优于GAEMDA,GRPAMDA和LRGCPND等现有模型.
对多克索鲁比和伊马替尼布的案例研究表明,B-NDRA在识别潜在的NDRA方面具有实际效用.

结论:

B-NDRA是一种强大的计算工具,用于发现ncRNA与药物耐药性的关联.
该框架集成相似性信息的能力提高了预测准确性.
通过帮助克服耐药性,B-NDRA具有促进癌症研究和治疗开发的巨大潜力.

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