Dataset Information

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction.

ABSTRACT: In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases. Therefore, predicting potential associations between miRNAs and diseases with the huge number of verified heterogeneous biological datasets will provide a new perspective for disease therapy. In this article, we developed a novel computational model of Triple Layer Heterogeneous Network based inference for MiRNA-Disease Association prediction (TLHNMDA) by using the experimentally verified miRNA-disease associations, miRNA-long noncoding RNA (lncRNA) interactions, miRNA function similarity information, disease semantic similarity information and Gaussian interaction profile kernel similarity for lncRNAs into an triple layer heterogeneous network to predict new miRNA-disease associations. As a result, the AUCs of TLHNMDA are 0.8795 and 0.8795 ± 0.0010 based on leave-one-out cross validation (LOOCV) and 5-fold cross validation, respectively. Furthermore, TLHNMDA was implemented on three complex human diseases to evaluate predictive ability. As a result, 84% (kidney neoplasms), 78% (lymphoma) and 76% (prostate neoplasms) of top 50 predicted miRNAs for the three complex diseases can be verified by biological experiments. In addition, based on the HMDD v1.0 database, 98% of top 50 potential esophageal neoplasms-associated miRNAs were confirmed by experimental reports. It is expected that TLHNMDA could be a useful model to predict potential miRNA-disease associations with high prediction accuracy and stability.

SUBMITTER: Chen X

PROVIDER: S-EPMC6038677 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction.

Chen Xing X Qu Jia J Yin Jun J

Frontiers in genetics 20180703

In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases. Therefore, predicting potential associations between miRNAs and diseases with the huge number of verified heterogeneous biological datasets will provide a new perspective for disease therapy. In this article, we developed a novel computational model of Triple Layer Heterogeneous Network based inference for MiRNA-Disease Associa ...[more]

PMID: 30018632

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Project description:Recently, a growing number of biological research and scientific experiments have demonstrated that microRNA (miRNA) affects the development of human complex diseases. Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases. However, since uncovering associations via experimental methods is expensive and time-consuming, novel and effective computational methods for association prediction are in demand. In this study, we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network. Compared with previous computational models based on heterogeneous networks, our model took full advantage of matrix decomposition before the construction of heterogeneous network, thereby improving the prediction accuracy. MDHGI obtained AUCs of 0.8945 and 0.8240 in the global and the local leave-one-out cross validation, respectively. Moreover, the AUC of 0.8794+/-0.0021 in 5-fold cross validation confirmed its stability of predictive performance. In addition, to further evaluate the model's accuracy, we applied MDHGI to four important human cancers in three different kinds of case studies. In the first type, 98% (Esophageal Neoplasms) and 98% (Lymphoma) of top 50 predicted miRNAs have been confirmed by at least one of the two databases (dbDEMC and miR2Disease) or at least one experimental literature in PubMed. In the second type of case study, what made a difference was that we removed all known associations between the miRNAs and Lung Neoplasms before implementing MDHGI on Lung Neoplasms. As a result, 100% (Lung Neoplasms) of top 50 related miRNAs have been indexed by at least one of the three databases (dbDEMC, miR2Disease and HMDD V2.0) or at least one experimental literature in PubMed. Furthermore, we also tested our prediction method on the HMDD V1.0 database to prove the applicability of MDHGI to different datasets. The results showed that 50 out of top 50 miRNAs related with the breast neoplasms were validated by at least one of the three databases (HMDD V2.0, dbDEMC, and miR2Disease) or at least one experimental literature.

Project description:MotivationOver the past few years, experimental evidence has highlighted the role of microRNAs to human diseases. miRNAs are critical for the regulation of cellular processes, and, therefore, their aberration can be among the triggering causes of pathological phenomena. They are just one member of the large class of non-coding RNAs, which include transcribed ultra-conserved regions (T-UCRs), small nucleolar RNAs (snoRNAs), PIWI-interacting RNAs (piRNAs), large intergenic non-coding RNAs (lincRNAs) and, the heterogeneous group of long non-coding RNAs (lncRNAs). Their associations with diseases are few in number, and their reliability is questionable. In literature, there is only one recent method proposed by Yang et al. (2014) to predict lncRNA-disease associations. This technique, however, lacks in prediction quality. All these elements entail the need to investigate new bioinformatics tools for the prediction of high quality ncRNA-disease associations. Here, we propose a method called ncPred for the inference of novel ncRNA-disease association based on recommendation technique. We represent our knowledge through a tripartite network, whose nodes are ncRNAs, targets, or diseases. Interactions in such a network associate each ncRNA with a disease through its targets. Our algorithm, starting from such a network, computes weights between each ncRNA-disease pair using a multi-level resource transfer technique that at each step takes into account the resource transferred in the previous one.ResultsThe results of our experimental analysis show that our approach is able to predict more biologically significant associations with respect to those obtained by Yang et al. (2014), yielding an improvement in terms of the average area under the ROC curve (AUC). These results prove the ability of our approach to predict biologically significant associations, which could lead to a better understanding of the molecular processes involved in complex diseases.AvailabilityAll the ncPred predictions together with the datasets used for the analysis are available at the following url: http://alpha.dmi.unict.it/ncPred/

Dataset Information

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction.

Publications

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction.

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