基于图形神经网络的circRNA相关疾病的计算模型:用于后续实验验证的预测和案例研究
Mengting Niu1,2, Chunyu Wang3, Zhanguo Zhang4
1School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China.
BMC biology
|January 28, 2024
概括
这项研究介绍了CircDA,这是一种新的计算工具,可以使用多组学数据预测循环RNA (circRNA) 和疾病之间的关联. CircDA准确地识别了新型circRNA与疾病的联系,有助于疾病机制研究和治疗开发.
科学领域:
- 生物化学 生物化学
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 循环RNAs (circRNAs) 在疾病发展中至关重要.
- 了解circRNA与疾病的联系对于疾病病因和治疗至关重要.
- 之前的工作开发了GMNN2CD算法用于circRNA疾病关联预测.
研究的目的:
- 开发一个更新的Web服务器CircDA,用于预测circRNA与疾病的关联.
- 将多源生物数据纳入circRNA疾病关联预测中.
- 用人类肝细胞癌 (HCC) 数据验证CircDA的预测.
主要方法:
- CircDA使用基于Tumarkov的深度学习框架.
- 它将多态数据建模为一个异质的生物分子关联网络,分子作为节点,相互作用作为边缘.
- 该方法抽象和整合各种生物数据,以表示复杂的分子关系.
主要成果:
- CircDA成功地确定了已知的circRNAs与HCC,宫癌和胃癌中的疾病之间的缺失关联.
- 在HCC组织中使用RT-qPCR的实验验证证证了五种circRNAs的显著差异性表达.
- 这些发现证明了CircDA能够预测新型circRNA疾病关联的能力.
结论:
- CircDA提供了一种有效的计算方法,用于识别circRNA相关的疾病和疾病相关的circRNAs.
- 该工具为探索circRNA在疾病中的作用提供了有价值的指导.
- 可访问的在线服务器和开源代码可用于更广泛的使用和进一步的算法开发.
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