PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction

Abstract

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

“…HGIMDA[17], EGBMMDA[33], PBMDA[21], MKRMD[29]), all of which have also achieved excellent performances in predicting potential miRNA-disease associations. As mentioned above, HGIMDA was an efficient prediction framework based on heterogeneous graph inference.…”

“…Concretely, PBMDA adopted a depth-first search algorithm to search paths of certain lengths for given miRNA-disease pairs on a heterogeneous graph and obtained comparable performance. However, the computational complexity of PBMDA could be extremely high in large networks[21]. Chen et al proposed NDAMDA to predict miRNA-disease associations based on network distance analysis.…”

SNMDA: A novel method for predicting microRNA‐disease associations based on sparse neighbourhood

“…If the ranking of the test sample was higher than a given threshold, it was marked as a successful prediction. In comparison, in the framework of local LOOCV, each known miRNA associated with a given disease was left out in turn as the test sample and the ranking of that test sample was only compared with the unconfirmed associations of this specific disease 26. Both frameworks were repeated 5430 times.…”

“…To demonstrate the effectiveness of our method, we applied global LOOCV, local LOOCV and 5‐fold cross‐validation to evaluate the performance of our method, respectively. The corresponding AUCs are 0.936, 0.882 and 0.934, which in all cases outperform the four state‐of‐the‐art methods (HGIMDA,23 PBMDA,26 EGBMMDA 34 and MKRMDA33). Moreover, three types of case studies on five common neoplasms further validated the effectiveness of our method.…”

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

“…The parameter ℎ was set to be 1 according to previous study [27]. Moreover, GIP is widely used in other studies [10,34,37,50,51]; we also set the values of both and to be 1. All results were validated over external validation of ExGPCRs datasets based on substructures MACCS and Graph.…”