GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Yu, Shengsheng; Liang, Cheng; Xiao, Qiu; Li, Guanghui; Ding, Pingjian; Luo, Jiawei

doi:10.1080/15476286.2018.1521210

Cited by 18 publications

(13 citation statements)

References 71 publications

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“…MiRNA functional similarity scores were computed based on the assumption that functionally similar miRNAs are more likely to connect with phenotypically similar disease . In this paper, we downloaded the miRNA functional similarity scores directly from http://http://www.cuilab.cn/files/images/cuilab/misim.zip.…”

Section: Methodsmentioning

confidence: 99%

“…MiRNA functional similarity scores were computed based on the assumption that functionally similar miRNAs are more likely to connect with phenotypically similar disease. 32,33 In this paper, we downloaded the miRNA functional similarity scores directly from http://www.cuilab.cn/files/images/cuilab/misim.zip. We used matrix FM to denote the obtained miRNA functional similarity network, in which FM(i,j) indicates the similarity between miRNA m(i) and miRNA m(j).…”

Section: Mirna Functional Similaritymentioning

confidence: 99%

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MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

Liang

Xiao

et al. 2018

J Cellular Molecular Medi

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MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.

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Section: Methodsmentioning

confidence: 99%

Section: Mirna Functional Similaritymentioning

confidence: 99%

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

Liang

Xiao

et al. 2018

J Cellular Molecular Medi

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“…3) Predicting microRNA-disease associations based on sparse neighborhoods (SNMDA): Qu et al (2018a) presented a method named SNMDA that takes advantage of the sparsity of the miRNA-disease association network and integrates the sparse information into the current similarity matrices for both miRNAs and diseases. 4) MiRNA-disease association prediction based on global linear neighborhoods (GLNMDA): Yu et al (2018) proposed a novel method that obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNAdisease associations and then adopts label propagation to infer the potential associations between miRNAs and diseases. 5) Predicting miRNA gene and disease relationship based on locality-constrained linear coding (LLCMDA): Qu et al (2018b) proposed LLCMDA.…”

Section: Inputmentioning

confidence: 99%

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction

Zhu

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Due to the cost and complexity of biological experiments, many computational methods have been proposed to predict potential miRNA-disease associations by utilizing known miRNA-disease associations and other related information. However, there are some challenges for these computational methods. First, the relationships between miRNAs and diseases are complex. The computational network should consider the local and global influence of neighborhoods from the network. Furthermore, predicting disease-related miRNAs without any known associations is also very important. This study presents a new computational method that constructs a heterogeneous network composed of a miRNA similarity network, disease similarity network, and known miRNA-disease association network. The miRNA similarity considers the miRNAs and their possible families and clusters. The information of each node in heterogeneous network is obtained by aggregating neighborhood information with graph convolutional networks (GCNs), which can pass the information of a node to its intermediate and distant neighbors. Disease-related miRNAs with no known associations can be predicted with the reconstructed heterogeneous matrix. We apply 5-fold cross-validation, leave-one-disease-out cross-validation, and global and local leave-one-out cross-validation to evaluate our method. The corresponding areas under the curves (AUCs) are 0.9616, 0.9946, 0.9656, and 0.9532, confirming that our approach significantly outperforms the state-of-the-art methods. Case studies show that this approach can effectively predict new diseases without any known miRNAs.

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“…used an ensemble model where a sequence of weak learners were trained to collectively obtain a predicted association score[19]. Recently, we reconstructed the miRNA and disease similarity matrices based on global linear neighborhoods and then applied label propagation to predict potential associations between diseases and miRNAs[20, 21]. Chen et al .…”

Section: Introductionmentioning

confidence: 99%

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs

Liang

Luo

2019

PLoS Comput Biol

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Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieved comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs.

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GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Cited by 18 publications

References 71 publications

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs

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