Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations

Abstract

Background: MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are time-consuming and costly. Objective: Thus, computational methods are urgently needed to effectively predict miRNA-disease associations. Method: In this paper, we proposed a novel method, BIRWMDA based on a bi-random walk model to predict miRNAdisease associations. Specifically, in BIRWMDA, the similarity network fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA similarity matrix and a disease-disease similarity matrix, then the miRNAdisease associations were predicted by the bi-random walk model. Results: To evaluate the performance of BIRWMDA, we ran the leave-one-out cross validation and 5-fold cross validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067, respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were confirmed by literatures, respectively. From the perspective of exploring miRNA-related diseases, we conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted diseases were confirmed by literatures, respectively. Conclusion: Fusion of multiple biological networks could effectively predict miRNA-diseases associations. We expected BIRWMDA to severe as a biological tool for mining potential miRNA-disease associations.

Identification of miRNA Signature Associated With Erectile Dysfunction in Type 2 Diabetes Mellitus by Support Vector Machine-Recursive Feature Elimination

“…For the robustness of feature selection, we refer to the method of Kai-Bo Duan ( Duan et al, 2005 ). We used ten-fold cross-validation here by adding resampling to each iteration to stabilize the ranking ( Zhu et al, 2021b ). After 1,000 cycles of the algorithm ( Supplementary Table S1 ), four characteristic miRNAs (hsa-let-7E-5p, hsa-miR-30 days-5p, hsa-miR-199b-5p, and hsa-miR-342–3p) were obtained according to the lowest error rate of 0.25 in ten-fold cross-validation ( Figure 1 ).…”

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

“…To evaluate the performance of the proposed method, we introduced four indicators commonly used in bioinformatics: sensitivity (SE), specificity (SP), accuracy (ACC), and Matthew’s correlation coefficient (MCC). The formulae of these indicators are as follows ( Zhang et al, 2021a ; Lv et al, 2021b ; Zhang et al, 2021b ; Zhang et al, 2021c ; Zhang et al, 2021d ; Zhang et al, 2021e ; Zhao et al, 2021 ; Zhu et al, 2021 ; Zou et al, 2021 ; Zhao et al, 2022 ). where TP is an abbreviation for true positives, representing the number of MHC proteins predicted in positive examples; FP is an abbreviation for false positives, representing the number of MHC proteins predicted in negative examples; TN is an abbreviation for true negatives, representing nonMHC proteins predicted in negative examples; and FN is an abbreviation for false negatives and indicates the number of predicted nonMHC proteins in positive examples.…”

iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank

“…The disease semantic similarity calculation is a key component in RNA-disease association identification. The disease ontology [47] has been applied to RNA-disease association identification so as to calculate disease semantic similarities [48][49][50][51][52][53]. Disease ontology organized by the directed acyclic graph (DAG) provides a hierarchical structure of the complex disease parent node [47].…”