Xu Long, Li Xiaokun, Yang Qiang, Tan Long, Liu Qingyuan, Liu Yong
School of Computer Science and Technology, Heilongjiang University, Harbin, China.
Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Heilongjiang University, Harbin, China.
Front Genet. 2022 Jul 12;13:936823. doi: 10.3389/fgene.2022.936823. eCollection 2022.
Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA-disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing computational models to facilitate the progress of disease pathology and clinical medicine by identifying the potential disease-related miRNAs. However, most existing computational methods are expensive, and their use is limited to unobserved relationships for unknown miRNAs (diseases) without association information. In this manuscript, we proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNA-disease association prediction (BGANMDA). First, we constructed a microRNA similarity network, a disease similarity network, and Gaussian interaction profile kernel similarity based on the known miRNA-disease association and comprehensive similarity of miRNAs (diseases). Next, an integrated similarity feature network with the full underlying relationships of miRNA-disease pairwise was obtained. Then, the similarity feature network was fed into the BGANMDA model to learn advanced traits in latent space. Finally, we ranked an association score list and predicted the associations between miRNA and disease. In our experiment, a five-fold cross validation was applied to estimate BGANMDA's performance, and an area under the curve (AUC) of 0.9319 and a standard deviation of 0.00021 were obtained. At the same time, in the global and local leave-one-out cross validation (LOOCV), the AUC value and standard deviation of BGANMDA were 0.9116 ± 0.0025 and 0.8928 ± 0.0022, respectively. Furthermore, BGANMDA was employed in three different case studies to validate its prediction capability and accuracy. The experimental results of the case studies showed that 46, 46, and 48 of the top 50 prediction lists had been identified in previous studies.
大量证据表明，微小RNA对于复杂人类疾病中的生物学过程至关重要。识别微小RNA与疾病的配对关系将有助于加速潜在生物标志物和发病机制的发现。研究人员开始专注于构建计算模型，通过识别潜在的疾病相关微小RNA来推动疾病病理学和临床医学的进展。然而，大多数现有的计算方法成本高昂，并且其应用仅限于没有关联信息的未知微小RNA（疾病）的未观察到的关系。在本论文中，我们提出了一种创新性的半监督模型，即用于微小RNA-疾病关联预测的双向生成对抗网络（BGANMDA）。首先，我们基于已知的微小RNA-疾病关联以及微小RNA（疾病）的综合相似性，构建了一个微小RNA相似性网络、一个疾病相似性网络和高斯相互作用轮廓核相似性。接下来，获得了一个具有微小RNA-疾病成对完整潜在关系的综合相似性特征网络。然后，将相似性特征网络输入到BGANMDA模型中，以在潜在空间中学习高级特征。最后，我们对关联得分列表进行排序，并预测微小RNA与疾病之间的关联。在我们的实验中，应用五折交叉验证来评估BGANMDA的性能，获得了曲线下面积（AUC）为0.9319，标准差为0.00021。同时，在全局和局部留一法交叉验证（LOOCV）中，BGANMDA的AUC值和标准差分别为0.9116±0.0025和0.8928±0.0022。此外，BGANMDA被应用于三个不同的案例研究中，以验证其预测能力和准确性。案例研究的实验结果表明，在前50个预测列表中，分别有46、46和48个在先前的研究中已被识别。
