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HoRDA:学习高阶结构信息,用于预测RNA与疾病的关联

Julong Li¹, Jianrui Chen¹, Zhihui Wang¹

¹School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.

Artificial intelligence in medicine

|February 7, 2024

概括

本研究介绍了HoRDA,这是一种深度学习方法,通过分析高阶网络结构来增强RNA疾病关联预测. 霍尔达提高了准确性,并捕捉了复杂的生物关系,以获得更好的疾病关联洞察力.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

循环RNA (CircRNAs) 和微RNA (miRNAs) 是关键的非编码RNA,涉及疾病.
预测RNA与疾病的关联是至关重要的,但由于复杂性和成本而具有挑战性.
现有的深度学习方法缺乏通用准确性,无法捕获更高阶的拓信息.

研究的目的:

开发一种新的深度学习框架,HoRDA,用于准确的RNA疾病关联预测.
为了提高预测,利用生物网络中的更高阶结构信息.
解决现有方法在普遍性和准确性方面的局限性.

主要方法:

利用一个更高阶的图表注意力网络来探索RNA和疾病之间的相关性.
采用高阶图形卷积网络来汇总邻居信息并导出RNA/疾病表示.
实施了更高阶负采样策略,以产生有效的负样本.
整合RNA和疾病嵌入到用于概率预测的逻辑回归模型中.

主要成果:

与现有方法相比,HoRDA在各种模拟中表现出优越的性能.
该方法有效地捕获了对RNA疾病关联至关重要的高阶拓信息.
关于乳腺,结直肠和胃瘤的案例研究证实了HoRDA的实际适用性和有效性.

结论:

通过结合高阶网络结构,HoRDA在预测RNA疾病关联方面取得了重大进展.
提出的更高层次的策略提高了预测的准确性和普遍性.
霍尔达为生物研究和疾病关联研究提供了有价值的工具.

关键词:

图表注意力网络图表注意力网络图表卷积网络卷积网络高阶结构的结构是高阶的.RNA疾病的关联.

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