PMDAGS:预测miRNA-Disease关联与图形非线性扩散卷积网络和相似之处
IEEE/ACM transactions on computational biology and bioinformatics
|February 15, 2024
概括
一种新的计算方法,PMDAGS,有效地使用图形非线性扩散卷积网络预测microRNA疾病关联. 这种方法通过识别潜在的生物标志物来改善疾病诊断和预后.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 微RNAs (miRNAs) 是生物过程的关键调节者,并作为潜在的非侵入性生物标志物用于疾病诊断和预后.
- 计算方法对于识别miRNA与疾病的关联至关重要,以改善疾病管理.
研究的目的:
- 引入PMDAGS,一种用于预测miRNA-疾病关联的新计算方法.
- 利用图形非线性扩散卷积网络和相似性测量来提高预测准确度.
主要方法:
- 使用miRNA-目标相互作用,基因-疾病关联以及已知的miRNA-疾病关联计算了miRNA和疾病相似性.
- 通过将相似向量与已知的关联向量结合起来,构建了初始节点特征.
- 应用非线性扩散图卷积网络来提取特征嵌入,然后使用多层感知子来进行关联预测.
主要成果:
- PMDAGS实现了高的曲线下面积 (AUC) 值,包括HMDD v2.0上的0.9222 (5倍交叉验证) 和HMDD v3.2.2.0上的0.9366 (5倍交叉验证).
- 该方法在预测miRNA-疾病关联方面,与现有的计算方法相比,表现优越.
- 交叉验证结果 (5CV,10CV,GLOOCV) 在HMDD v2.0和HMDD v3.2数据集上一致验证了PMDAGS的有效性.
结论:
- PMDAGS有效地预测了潜在的miRNA疾病关联.
- 提出的方法为生物标志物发现和疾病关联研究的计算方法提供了显著的进步.
- PMDAGS的性能优于现有方法,突出了其在疾病诊断和预后中临床应用的潜力.
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