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Updated: May 13, 2025

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解开与疾病相关的PIWI交互RNA与对比的学习方法

Xiaowen Hu¹, Hao Sun¹, Linchao Shan¹

¹School of Computer Science and Engineering, Center South University, Changsha 410083, China.

Journal of chemical information and modeling

|April 23, 2025

概括

一个新的计算模型,iPiDA_CL,有效地预测PIWI-交互RNA (piRNA) -疾病关联,没有负样本. 这一进步有助于发现癌症等疾病的生物标志物.

科学领域:

基因组学和分子生物学
生物信息学和计算生物学
生物医学研究的研究.

背景情况:

与PIWI相互作用的RNAs (piRNAs) 对基因组完整性和基因稳定性至关重要.
piRNAs与包括癌症在内的人类疾病有关,突出显示了它们作为生物标记物的潜力.
目前用于识别piRNA疾病关联的湿实验室方法资源密集且效率低下.

研究的目的:

开发一种新的计算模型,用于预测piRNA与疾病的关联.
在piRNA-疾病关联研究中解决数据稀疏性挑战.
为识别潜在的piRNA-疾病联系提供一个高效和准确的工具.

主要方法:

拟议的iPiDA_CL模型使用对比学习,没有负样本.
通过高斯核相似性表示piRNA-疾病关联作为一个双部分图形,通过Gaussian核相似性进行初始嵌入.
采用LightGCN进行功能更新和一个有数据增强的Siamese网络框架进行对比学习.

主要成果:

与最先进的方法相比,iPiDA_CL表现出卓越的性能和计算效率.
该模型的多功能性得到了通过其成功应用于miRNA-疾病关联预测的证实.
实验结果验证了iPiDA_CL作为piRNA-疾病关联发现的有希望的工具.

结论:

iPiDA_CL提供了一种有效的解决方案,用于预测piRNA与疾病的关联,克服数据的限制.
该模型的对比学习方法提高了预测准确性和效率.
iPiDA_CL具有通过生物标志物识别来推进疾病诊断和治疗策略的巨大潜力.

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