Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Abstract

Predicting beneficial and valuable miRNA–disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA–disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). We used a WKNKN algorithm as a pre-processing step to solve the problem of sparsity and incompletion of data to reduce the negative impact of a large number of missing associations. Two heterogeneous networks in disease and miRNA spaces were built by integrating multiple similarity networks, respectively, and different walk probabilities could be designated to each linked neighbor node of the disease or miRNA node in line with its degree in respective networks. Finally, an improve extended random walk with restart algorithm based on miRNA similarity-based and disease similarity-based heterogeneous networks was used to calculate miRNA–disease association prediction probabilities. The experiments showed that our proposed method achieved a momentous performance with Global LOOCV AUC (Area Under Roc Curve) and AUPR (Area Under Precision-Recall Curve) values of 0.9882 and 0.9066, respectively. And the best AUC and AUPR values under fivefold cross-validation of 0.9855 and 0.8642 which are proven by statistical tests, respectively. In comparison with other previous related methods, it outperformed than NTSHMDA, PMFMDA, IMCMDA and MCLPMDA methods in both AUC and AUPR values. In case studies of Breast Neoplasms, Carcinoma Hepatocellular and Stomach Neoplasms diseases, it inferred 1, 12 and 7 new associations out of top 40 predicted associated miRNAs for each disease, respectively. All of these new inferred associations have been confirmed in different databases or literatures.

iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data

“…The formulation of (1) is similar to the Gaussian Interaction Profile kernel, which has been widely used in previous studies that focused on similarity-based scoring configurations ( Lan et al 2017 , Jiang et al 2019 , Nguyen et al 2021 , Shakyawar et al 2022 ). In this study, it measures the similarity between two patients using the Euclidean distance between them.…”

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

“…RWR is the state-of-the-art approach to infer the relationship: as the name suggests, a random walker, starting from a set of nodes of interest (starting nodes), jumps to neighboring nodes, or nodes in another layer according to a certain probability assigned to the edges of the nodes ( Lee and Yoon, 2018 ). In addition, the walker has a certain probability, known as the damping factor, such that for each step taken in any direction, there is a probability associated with returning to one of the original sets of nodes ( Valdeolivas et al, 2019 ; Nguyen et al, 2021 ; Qu et al, 2021 ; Wen et al, 2021 ). The probability is calculated from a transition matrix from one node to the other, allowing to obtain a weight for each interaction.…”

A many‐objective optimization‐based local tensor factorization model for skin cancer detection

“…Qu et al 24 predict miRNA‐disease association by enforcing degree‐based biased random walk on a multi‐layer heterogeneous network, which is constructed using known miRNA‐disease association information. Nguyen et al 25 separately constructed heterogeneous networks for diseases and miRNAs and assigned them different roaming probabilities. They proposed predicting miRNA‐disease associations using modified random walks and restarts, and fusing multiple similarities.…”