Prediction of potential disease-associated microRNAs based on random walk

Xuan, Ping; Han, Koeun; Guo, Yahong; Li, Jin; Li, Xia; Zhong, Yingli; Zhang, Zhaogong; Ding, Jian

doi:10.1093/bioinformatics/btv039

Cited by 180 publications

(143 citation statements)

References 35 publications

Supporting

Mentioning

141

Contrasting

Order By: Relevance

“…Besides, HDMP is based on a local similarity measure rather than a global measure which can notably promote the prediction performance. Xuan et al [39] introduced another model called MIDP based on random walk, which exploited the characteristics of the nodes and the various ranges of topologies. The labeled nodes in MIDP were assigned higher transition weight than the unlabeled nodes, which efficiently exploited the prior information of nodes and various ranges of topologies.…”

Section: Introductionmentioning

confidence: 99%

MCMDA: Matrix completion for MiRNA-disease association prediction

et al. 2017

View full text Add to dashboard Cite

Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective method. In this study, we developed a Matrix Completion for MiRNA-Disease Association prediction model (MCMDA) based on the known miRNA-disease associations in HMDD database. MCMDA model utilized the matrix completion algorithm to update the adjacency matrix of known miRNA-disease associations and furthermore predict the potential associations. To evaluate the performance of MCMDA, we performed leave-one-out cross validation (LOOCV) and 5-fold cross validation to compare MCMDA with three previous classical computational models (RLSMDA, HDMP, and WBSMDA). As a result, MCMDA achieved AUCs of 0.8749 in global LOOCV, 0.7718 in local LOOCV and average AUC of 0.8767+/−0.0011 in 5-fold cross validation. Moreover, the prediction results associated with colon neoplasms, kidney neoplasms, lymphoma and prostate neoplasms were verified. As a consequence, 84%, 86%, 78% and 90% of the top 50 potential miRNAs for these four diseases were respectively confirmed by recent experimental discoveries. Therefore, MCMDA model is superior to the previous models in that it improves the prediction performance although it only depends on the known miRNA-disease associations.

show abstract

Section: Introductionmentioning

confidence: 99%

MCMDA: Matrix completion for MiRNA-disease association prediction

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Specifically, we applied three types of cross‐validations, namely, global leave‐one‐out cross‐validation (LOOCV), local LOOCV and 5‐fold cross‐validation. To prove the effectiveness of the algorithm, KFRLSMDA was compared with 10 previous computational methods: MaxFlow, RKNNMDA, MiRAI, HDMP, RWRMDA, WBSMDA, HGIMDA, RLSMDA, MIDP and MCMDA . In LOOCV evaluation, each known association in the database was considered as the test sample in turn while the other known associations were viewed as training samples.…”

Section: Resultsmentioning

confidence: 99%

“…These diseases were selected in our case studies because they are the most common cancer types, with high incidence and death rate each year. In addition, they have been used as case studies in many previous publications . Unlike cross‐validations that solely depended on HMDD v2.0, our case studies used HMDD v2.0 as the training database for KFRLSMDA and dbDEMC and miR2Disease as the validation databases for confirming the predicted potential associations.…”

Section: Resultsmentioning

confidence: 99%

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Guan

Wang

Zhang

et al. 2019

J Cellular Molecular Medi

View full text Add to dashboard Cite

Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion‐based Regularized Least Squares for MiRNA‐Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA‐disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave‐one‐out cross‐validation (LOOCV) and 5‐fold cross‐validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5‐fold cross‐validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.

show abstract

“…al . [34] developed a computational model of MI RNAs associated with D iseases P rediction (MIDP) and its extension version named MIDPE for the diseases with known related miRNAs and without any known related miRNAs, respectively. They established the transition matrices between the labeled and unlabeled nodes for exploring the prior information of nodes and the different ranges of topologies.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

Prediction of potential disease-associated microRNAs based on random walk

Abstract: A web service for the prediction and analysis of disease miRNAs is available at http://bioinfolab.stx.hk/midp/.

Cited by 180 publications

References 35 publications

MCMDA: Matrix completion for MiRNA-disease association prediction

MCMDA: Matrix completion for MiRNA-disease association prediction

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

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

Contact Info

Product

Resources

About