MicroRNAs (miRNAs) exert an enormous influence on cell differentiation, biological development and the onset of diseases. Because predicting potential miRNA-disease associations (MDAs) by biological experiments usually requires considerable time and money, a growing number of researchers are working on developing computational methods to predict MDAs. High accuracy is critical for prediction. To date, many algorithms have been proposed to infer novel MDAs. However, they may still have some drawbacks. In this paper, a logistic weighted profile-based bi-random walk method (LWBRW) is designed to infer potential MDAs based on known MDAs. In this method, three networks (i.e., a miRNA functional similarity network, a disease semantic similarity network and a known MDA network) are constructed first. In the process of building the miRNA network and the disease network, Gaussian interaction profile (GIP) kernel is computed to increase the kernel similarities, and the logistic function is used to extract valuable information and protect known MDAs. Next, the known MDA matrix is preprocessed by the weighted K-nearest known neighbours (WKNKN) method to reduce the number of false negatives. Then, the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network. Finally, the predictive ability of the LWBRW method is confirmed by the average AUC of 0.939 3 (0.006 1) in 5-fold cross-validation (CV) and the AUC value of 0.976 3 in leave-one-out cross-validation (LOOCV). In addition, case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

微小RNA（miRNA）对细胞分化，生物学发育和疾病发作产生巨大影响。由于通过生物学实验预测潜在的miRNA疾病关联（MDA）通常需要大量的时间和金钱，因此越来越多的研究人员正在研究开发预测MDA的计算方法。高精度对于预测至关重要。迄今为止，已经提出了许多算法来推断新颖的MDA。但是，它们可能仍然有一些缺点。在本文中，基于已知的MDA，设计了一种基于逻辑加权轮廓的双向随机游走法（LWBRW）来推断潜在的MDA。在这种方法中，首先构建了三个网络（即，miRNA功能相似性网络，疾病语义相似性网络和已知的MDA网络）。在建立miRNA网络和疾病网络的过程中，计算了高斯交互分布（GIP）内核以增加内核相似度，并使用逻辑函数提取有价值的信息并保护已知的MDA。接下来，通过加权对已知的MDA矩阵进行预处理K近邻已知（WKNKN）方法可减少假阴性的数量。然后，通过在miRNA网络和疾病网络上双随机行走，将LWBRW方法应用于推断新型MDA。最后，LWBRW方法的预测能力由5倍交叉验证（CV）中的平均AUC为0.939 3（0.006 1）和留一法交叉验证（LOOCV）中的AUC值为0.976 3证实了）。此外，案例研究还显示了LWBRW方法探究潜在MDA的出色能力。