College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China.
Brief Bioinform. 2023 Sep 20;24(5). doi: 10.1093/bib/bbad270.
Numerous biological studies have shown that considering disease-associated micro RNAs (miRNAs) as potential biomarkers or therapeutic targets offers new avenues for the diagnosis of complex diseases. Computational methods have gradually been introduced to reveal disease-related miRNAs. Considering that previous models have not fused sufficiently diverse similarities, that their inappropriate fusion methods may lead to poor quality of the comprehensive similarity network and that their results are often limited by insufficiently known associations, we propose a computational model called Generative Adversarial Matrix Completion Network based on Multi-source Data Fusion (GAMCNMDF) for miRNA-disease association prediction. We create a diverse network connecting miRNAs and diseases, which is then represented using a matrix. The main task of GAMCNMDF is to complete the matrix and obtain the predicted results. The main innovations of GAMCNMDF are reflected in two aspects: GAMCNMDF integrates diverse data sources and employs a nonlinear fusion approach to update the similarity networks of miRNAs and diseases. Also, some additional information is provided to GAMCNMDF in the form of a 'hint' so that GAMCNMDF can work successfully even when complete data are not available. Compared with other methods, the outcomes of 10-fold cross-validation on two distinct databases validate the superior performance of GAMCNMDF with statistically significant results. It is worth mentioning that we apply GAMCNMDF in the identification of underlying small molecule-related miRNAs, yielding outstanding performance results in this specific domain. In addition, two case studies about two important neoplasms show that GAMCNMDF is a promising prediction method.
大量的生物研究表明，将疾病相关的 microRNAs（miRNAs）视为潜在的生物标志物或治疗靶点，为复杂疾病的诊断提供了新的途径。计算方法已逐渐被引入以揭示与疾病相关的 miRNAs。考虑到之前的模型没有充分融合各种相似性，其不合适的融合方法可能导致综合相似性网络的质量较差，并且其结果通常受到已知关联不足的限制，我们提出了一种称为基于多源数据融合的生成对抗矩阵补全网络（GAMCNMDF）的计算模型，用于 miRNA-疾病关联预测。我们创建了一个连接 miRNA 和疾病的多样化网络，然后使用矩阵表示。GAMCNMDF 的主要任务是完成矩阵并获得预测结果。GAMCNMDF 的主要创新体现在两个方面：GAMCNMDF 集成了多种数据源，并采用非线性融合方法更新 miRNA 和疾病的相似性网络。此外，还以“提示”的形式向 GAMCNMDF 提供了一些附加信息，以便即使在没有完整数据的情况下，GAMCNMDF 也能成功工作。与其他方法相比，在两个不同数据库上的 10 倍交叉验证结果验证了 GAMCNMDF 的卓越性能，结果具有统计学意义。值得一提的是，我们将 GAMCNMDF 应用于潜在小分子相关 miRNAs 的识别，在该特定领域取得了出色的性能结果。此外，关于两种重要肿瘤的两个案例研究表明，GAMCNMDF 是一种很有前途的预测方法。
