MNCLCDA:通过使用混合邻近信息和对比学习来预测circRNA-药物敏感性关联
Guanghui Li1, Feifan Zeng2, Jiawei Luo3
1School of Information Engineering, East China Jiaotong University, Nanchang, China. ghli16@hnu.edu.cn.
BMC medical informatics and decision making
|December 19, 2023
概括
这项研究介绍了MNCLCDA,这是一个计算框架,用于预测循环RNA (circRNA) 和药物敏感性之间的关联. MNCLCDA准确地识别了潜在的circRNA药物联系,有助于癌症研究和临床试验指导.
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.
- 药理学 药理学是指药理学的学科.
背景情况:
- 循环RNAs (circRNAs) 与癌症的发展和耐药性有关.
- circRNA表达影响细胞对治疗药物的敏感性,影响治疗疗效.
- 对circRNA与药物敏感性关系的实验验证是耗时且昂贵的.
研究的目的:
- 开发一个高效的计算框架,MNCLCDA,用于预测药物敏感性和circRNAs之间的潜在关联.
- 通过识别新型circRNA药物相互作用,提供一种协助医学研究的工具.
主要方法:
- MNCLCDA使用药物结构,circRNA序列和GIP内核信息来量化药物-circRNA相似性.
- 一个随机步行与重启方法预处理相似性网络以捕获基本特征.
- 混合社区图形卷积网络和基于图形的对比学习增强了节点社区信息和模型稳定性.
- 一种双拉普拉斯规范最小方程方法预测了circRNA与药物之间的关联.
主要成果:
- 与其他六种先进的计算方法相比,MNCLCDA表现出卓越的性能.
- 案例研究证实了MNCLCDA在预测实际cirRNA药物敏感性关联方面的能力.
结论:
- MNCLCDA 作为一个有效的计算工具,用于预测circRNA与药物之间的关联.
- 该框架为癌症治疗中的临床试验提供了有价值的指导.
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