RPMVCDA:随机扰动和多视图卷积网络用于CircRNA-疾病协会预测
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
这项研究介绍了RPMVCDA,这是一种用于预测循环RNA (circRNA) -疾病关联的新型计算模型. RPMVCDA通过整合多视图卷积网络和随机扰动来提高预测准确性,解决当前稀疏关联网络的局限性.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 循环RNAs (circRNAs) 在各种疾病中起着调节作用.
- 准确识别circRNA与疾病的关联对于了解疾病机制至关重要.
- 现有的计算模型面临的挑战是由于稀疏的circRNA-疾病关联网络.
研究的目的:
- 开发一种先进的计算模型,即RPMVCDA,用于预测circRNA与疾病的关联.
- 提高circRNA疾病关联预测的准确性和稳定性.
- 解决现有的circRNA疾病关联网络中的稀疏性问题.
主要方法:
- 拟议的RPMVCDA模型集成随机扰动和多视图卷积网络 (GCNs).
- 构建多个circRNA和疾病相似性网络,利用多视图GCN嵌入表示.
- 整合了一个特征相似性关联网络来传递消息,以及一个随机扰动关联网络来探索潜在的关联.
- 采用自我注意机制来生成高质量的功能,用于协会得分计算.
主要成果:
- 与现有模型相比,RPMVCDA在预测circRNA与疾病的关联方面表现优越.
- 在CircR2Disease数据集上进行了五次交叉验证和案例研究,验证了该模型的有效性.
- 提出的随机扰动方法有效地探索了潜在的circRNA疾病关联.
结论:
- RPMVCDA为预测circRNA与疾病的关联提供了一个有前途的替代方案.
- 该模型的性能突显了整合多视图GCN和随机扰动技术的潜力.
- 持续开发计算模型对于推进circRNA-疾病关联研究至关重要.
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