Dataset Information

Global network random walk for predicting potential human lncRNA-disease associations.

ABSTRACT: There is more and more evidence that the mutation and dysregulation of long non-coding RNA (lncRNA) are associated with numerous diseases, including cancers. However, experimental methods to identify associations between lncRNAs and diseases are expensive and time-consuming. Effective computational approaches to identify disease-related lncRNAs are in high demand; and would benefit the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In light of some limitations of existing computational methods, we developed a global network random walk model for predicting lncRNA-disease associations (GrwLDA) to reveal the potential associations between lncRNAs and diseases. GrwLDA is a universal network-based method and does not require negative samples. This method can be applied to a disease with no known associated lncRNA (isolated disease) and to lncRNA with no known associated disease (novel lncRNA). The leave-one-out cross validation (LOOCV) method was implemented to evaluate the predicted performance of GrwLDA. As a result, GrwLDA obtained reliable AUCs of 0.9449, 0.8562, and 0.8374 for overall, novel lncRNA and isolated disease prediction, respectively, significantly outperforming previous methods. Case studies of colon, gastric, and kidney cancers were also implemented, and the top 5 disease-lncRNA associations were reported for each disease. Interestingly, 13 (out of the 15) associations were confirmed by literature mining.

SUBMITTER: Gu C

PROVIDER: S-EPMC5622075 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Global network random walk for predicting potential human lncRNA-disease associations.

Gu Changlong C Liao Bo B Li Xiaoying X Cai Lijun L Li Zejun Z Li Keqin K Yang Jialiang J

Scientific reports 20170929 1

There is more and more evidence that the mutation and dysregulation of long non-coding RNA (lncRNA) are associated with numerous diseases, including cancers. However, experimental methods to identify associations between lncRNAs and diseases are expensive and time-consuming. Effective computational approaches to identify disease-related lncRNAs are in high demand; and would benefit the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In light of some limitations o ...[more]

PMID: 28963512

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Project description: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.

Project description:MOTIVATION:Human microbes play critical roles in drug development and precision medicine. How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays. Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing. Considering the high cost and risk of biological experiments, the computational approach is an alternative choice. However, at present, few computational approaches have been developed to tackle this task. RESULTS:In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network, and a microbe-drug interaction network. We then proposed a novel Graph Convolutional Network (GCN) based framework for predicting human Microbe-Drug Associations, named GCNMDA. In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (i.e., microbes or drugs) have similar representations. To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer. Moreover, we performed a random walk with restart (RWR) based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes respectively. Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods. Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (i.e., Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe-drug associations. AVAILABILITY:Python codes and dataset are available at: https://github.com/longyahui/GCNMDA. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Dataset Information

Global network random walk for predicting potential human lncRNA-disease associations.

Publications

Global network random walk for predicting potential human lncRNA-disease associations.

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