A network embedding-based multiple information integration method for the MiRNA-disease association prediction

Gong, Yuchong; Niu, Yanqing; Zhang, Wen; Xiao-hong, LI

doi:10.1186/s12859-019-3063-3

Cited by 59 publications

(42 citation statements)

References 57 publications

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“…Previous studies (Gong et al, 2019;Zhang et al, 2019a;Zhang et al, 2019b) have demonstrated the usefulness of similarities for network models. For convenience, let L be the set of lncRNAs, M the set of miRNAs, and P the set of proteins, e.g.…”

Section: Similarity Measuresmentioning

confidence: 99%

Predicting lncRNA–Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model

Zhou

Shen

et al. 2020

Front. Genet.

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Long non-coding RNAs (lncRNAs) play important roles in various biological processes, where lncRNA-protein interactions are usually involved. Therefore, identifying lncRNAprotein interactions is of great significance to understand the molecular functions of lncRNAs. Since the experiments to identify lncRNA-protein interactions are always costly and time consuming, computational methods are developed as alternative approaches. However, existing lncRNA-protein interaction predictors usually require prior knowledge of lncRNA-protein interactions with experimental evidences. Their performances are limited due to the number of known lncRNA-protein interactions. In this paper, we explored a novel way to predict lncRNA-protein interactions without direct prior knowledge. MiRNAs were picked up as mediators to estimate potential interactions between lncRNAs and proteins. By validating our results based on known lncRNA-protein interactions, our method achieved an AUROC (Area Under Receiver Operating Curve) of 0.821, which is comparable to the state-of-the-art methods. Moreover, our method achieved an improved AUROC of 0.852 by further expanding the training dataset. We believe that our method can be a useful supplement to the existing methods, as it provides an alternative way to estimate lncRNA-protein interactions in a heterogeneous network without direct prior knowledge. All data and codes of this work can be downloaded from GitHub (https://github.com/zyk2118216069/LncRNA-protein-interactions-prediction).

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Section: Similarity Measuresmentioning

confidence: 99%

Predicting lncRNA–Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model

Zhou

Shen

et al. 2020

Front. Genet.

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“…In our experiment, we used the following four indicators to evaluate the predictive performance of our proposed model, including Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Mathew's Correlation Coefficient (MCC). They are the four commonly used indicators for classifier performance evaluation in other Bioinformatics fields (Zhang et al, 2008(Zhang et al, , 2018a(Zhang et al, ,b,c, 2019bWei et al, 2017bWei et al, , 2019bZeng et al, 2017bZeng et al, , 2019cChen et al, 2018;Lu et al, 2018a,b;Fu et al, 2019;Gong et al, 2019;Jin et al, 2019;Liu and Li, 2019;Liu et al, 2019c,d;Manavalan et al, 2019a,b,c,d;Basith et al, 2020). Their calculation formulas are as follows:…”

Section: Performance Indicatorsmentioning

confidence: 99%

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

Zeng

Liao

2020

Front. Bioeng. Biotechnol.

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DNA N4-methylcytosine modification (4mC) plays an essential role in a variety of biological processes. Therefore, accurate identification the 4mC distribution in genome-scale is important for systematically understanding its biological functions. In this study, we present Deep4mcPred, a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications. In this predictor, we for the first time integrate residual network and recurrent neural network to build a multi-layer deep learning predictive system. As compared to existing predictors using traditional machine learning, our proposed method has two advantages. First, our deep learning framework does not need to specify the features when training the predictive model. It can automatically learn the high-level features and capture the characteristic specificity of 4mC sites, benefiting to distinguish true 4mC sites from non-4mC sites. On the other hand, our deep learning method outperforms the traditional machine learning predictors in performance by benchmarking comparison, demonstrating that the proposed Deep4mcPred is more effective in the DNA 4mC site prediction. Moreover, via experimental comparison, we found that attention mechanism introduced into the deep learning framework is useful to capture the critical features. Additionally, we develop a webserver implementing the proposed method for the academic use of research community, which is now available at http://server.malab.cn/Deep4mcPred.

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“…As described in Wang et al (2010) Gong et al (2019), Zhang et al (2019b), the hierarchical MeSH descriptors of diseases can be compiled as Directed Acyclic Graphs (DAGs), where vertexes represent the diseases and edges represent the relationships between different diseases. For a disease d, the DAG is denoted…”

Section: Disease-disease Similaritymentioning

confidence: 99%

Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

Huang

Yang

et al. 2020

Front. Bioeng. Biotechnol.

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Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease associations involve different association types, such as drug indications and drug side effects. However, the existing models for predicting drug-disease associations merely concentrate on independent tasks: recommending novel indications to benefit drug repositioning, predicting potential side effects to prevent drug-induced risk, or only determining the existence of drug-disease association. They ignore crucial prior knowledge of the correlations between different association types. Since the Comparative Toxicogenomics Database (CTD) annotates the drug-disease associations as therapeutic or marker/mechanism, we consider predicting the two types of association. To this end, we propose a collective matrix factorization-based multi-task learning method (CMFMTL) in this paper. CMFMTL handles the problem as multi-task learning where each task is to predict one type of association, and two tasks complement and improve each other by capturing the relatedness between them. First, drug-disease associations are represented as a bipartite network with two types of links representing therapeutic effects and non-therapeutic effects. Then, CMFMTL, respectively, approximates the association matrix regarding each link type by matrix tri-factorization, and shares the low-dimensional latent representations for drugs and diseases in the two related tasks for the goal of collective learning. Finally, CMFMTL puts the two tasks into a unified framework and an efficient algorithm is developed to solve our proposed optimization problem. In the computational experiments, CMFMTL outperforms several state-of-the-art methods both in the two tasks. Moreover, case studies show that CMFMTL helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their association types.

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A network embedding-based multiple information integration method for the MiRNA-disease association prediction

Cited by 59 publications

References 57 publications

Predicting lncRNA–Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model

Predicting lncRNA–Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

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