Computer Science and Artificial Intelligence and Aliyun School of Big Data, Changzhou University, Changzhou, China.
Life Sciences, Inner Mongolia Agricultural University, Hohhot, China.
J Comput Biol. 2024 Mar;31(3):241-256. doi: 10.1089/cmb.2023.0266. Epub 2024 Feb 19.
More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.
越来越多的研究表明，微小 RNA（miRNA）在人类复杂疾病的研究中起着不可或缺的作用。传统的生物实验来检测 miRNA-疾病关联既昂贵又耗时。因此，有必要提出有效的、有意义的计算模型来预测 miRNA-疾病关联。在这项研究中，我们旨在提出一种基于稀疏学习和多层随机游走（SLMRWMDA）的 miRNA-疾病关联预测模型。通过稀疏学习方法对 miRNA-疾病关联矩阵进行分解和重构，以获得更丰富的关联信息，同时获得随机游走再启动算法的初始概率矩阵。利用疾病相似性网络、miRNA 相似性网络和 miRNA-疾病关联网络构建异质网络，并基于疾病和 miRNA 的拓扑结构特征通过多层随机游走算法得到稳定概率，从而预测 miRNA-疾病潜在关联。实验结果表明，与之前的相关模型相比，该模型的预测精度有了显著提高。我们使用全局留一法交叉验证（global LOOCV）和五重交叉验证（5-fold CV）来评估模型。LOOCV 的曲线下面积（AUC）值为 0.9368。5-fold CV 的平均 AUC 值为 0.9335，方差为 0.0004。在案例研究中，结果表明 SLMRWMDA 有效地推断了 miRNA-疾病的潜在关联。
