A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations

Jiechen Li,Bihai Zhao,Bing Wang,Lei Wang,Xueyong Li,Xiang Feng

doi:10.1186/s12859-019-3216-4

Abstract

BackgroundIn recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health. However, most of the lncRNA-disease associations have not been found yet due to high costs and time complexity of traditional bio-experiments. Hence, it is quite urgent and necessary to establish efficient and reasonable computational models to predict potential associations between lncRNAs and diseases.ResultsIn this manuscript, a novel prediction model called TCSRWRLD is proposed to predict potential lncRNA-disease associations based on improved random walk with restart. In TCSRWRLD, a heterogeneous lncRNA-disease network is constructed first by combining the integrated similarity of lncRNAs and the integrated similarity of diseases. And then, for each lncRNA/disease node in the newly constructed heterogeneous lncRNA-disease network, it will establish a node set called TCS (Target Convergence Set) consisting of top 100 disease/lncRNA nodes with minimum average network distances to these disease/lncRNA nodes having known associations with itself. Finally, an improved random walk with restart is implemented on the heterogeneous lncRNA-disease network to infer potential lncRNA-disease associations. The major contribution of this manuscript lies in the introduction of the concept of TCS, based on which, the velocity of convergence of TCSRWRLD can be quicken effectively, since the walker can stop its random walk while the walking probability vectors obtained by it at the nodes in TCS instead of all nodes in the whole network have reached stable state. And Simulation results show that TCSRWRLD can achieve a reliable AUC of 0.8712 in the Leave-One-Out Cross Validation (LOOCV), which outperforms previous state-of-the-art results apparently. Moreover, case studies of lung cancer and leukemia demonstrate the satisfactory prediction performance of TCSRWRLD as well.ConclusionsBoth comparative results and case studies have demonstrated that TCSRWRLD can achieve excellent performances in prediction of potential lncRNA-disease associations, which imply as well that TCSRWRLD may be a good addition to the research of bioinformatics in the future.

Highlights

In recent years, long Non-coding RNAs (ncRNAs) (lncRNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health
In order to verify the performance of TCSRWRLD in predicting potential long ncRNAs (lncRNAs)-disease associations, Leave-One-Out Cross Validation (LOOCV), 2folds 5-fold cross-validation (CV), 5-folds CV and 10-folds CV were implemented on TCSRWRLD respectively
Based on the dataset of 2017-version downloaded from the lncRNADisease database and the dataset of 2016-version downloaded from the lnc2Cancer database, we compared TCSRWRLD with state-of-the-art prediction models such as KATZLDA, PMFILDA [38] and Ping’s model separately

Summary

Introduction

LncRNAs (long-non-coding RNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health. Most of the lncRNA-disease associations have not been found yet due to high costs and time complexity of traditional bio-experiments. Since gene mapping approach is extremely timeconsuming and labor-intensive, researches in the field of lncRNAs have been at a relatively slow pace for a long time [10, 11]. With the rapid development of high-throughput technologies in gene sequencing, more and more lncRNAs have been found in eukaryotes and other species [12, 13]. Growing evidences have further illustrated that lncRNAs are closely linked to diseases that pose a serious threat to human health [16,17,18], which means that lncRNAs can be used as potential biomarkers in the course of disease treatment in the future [19]

Methods

Results

Conclusion