Dual-network sparse graph regularized matrix factorization for predicting miRNA–disease associations

Gao, Mingming; Cui, Zhen; Gao, Ying-Lian; Liu, Jin-Xing; Zheng, Chun-Hou

doi:10.1039/c8mo00244d

Cited by 23 publications

(15 citation statements)

References 28 publications

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“…Finally, the AUCs of GRL 2, 1 -NMF are higher than those of some excellent models. Note that DNSGRMF [53], which also predicts miRNA-disease connections, is a graph regularized method similar to GRL 2, 1 -NMF. Both methods decompose the original matrix Y into two matrices W and H, and then we can acquire a recovery matrix Y * = W * H. It is worth noting that GRL 2, 1 -NMF is based on nonnegative factorization, while DNSGRMF is based on graph regularized matrix factorization.…”

Section: Discussionmentioning

confidence: 99%

“…3 Flow chart of GRL2,1-NMF leveraging the geometric structure of the data [56]. The L 2, 1 -norm was added to increase the disease matrix sparsity and eliminate unattached disease pairs [30,52,53]. The optimization problem of GRL 2, 1 -NMF can be formularized as follows:…”

Section: Standard Nmfmentioning

confidence: 99%

“…In addition, unlike the traditional nonnegative matrix factorization (NMF) methods, we added L 2, 1norm as well as GIP (Gaussian interaction profile) kernels into the NMF model. The L 2, 1 -norm was added to increase the disease matrix sparsity and eliminate unattached disease pairs [51][52][53]. Moreover, Tikhonov regularization was added to penalize the nonsmoothness of W and H [48,54,55], and the graph regularization was primarily intended to assure localbased representation by leveraging the geometry of the data [56].…”

Section: Introductionmentioning

confidence: 99%

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Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

Gao

Wang

et al. 2020

BMC Bioinformatics

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Background: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers.Results: Here, we present a computational framework based on graph Laplacian regularized L 2, 1 -nonnegative matrix factorization (GRL 2, 1 -NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL 2,1 -NMF framework was used to predict links between microRNAs and diseases. Conclusions:The new method (GRL 2, 1 -NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL 2, 1 -NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

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Section: Discussionmentioning

confidence: 99%

Section: Standard Nmfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

Gao

Wang

et al. 2020

BMC Bioinformatics

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“…At the same time, they provide scoring schemes that can create correlation sets of high and medium credibility. L − norm and Gaussian interaction profile (GIP) kernel to improve the prediction ability [25]. In addition, considering the nearest neighbor information of the miRNA and the disease,…”

Section: Yin Et Al Proposed a New A Computational Model Of Matrix Dementioning

confidence: 99%

MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

Yin

Jiao

et al. 2020

Preprint

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Background: microRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix to form the GIP kernel similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005).Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

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“…The discovery of extensive transcription of large RNA transcripts which do not code for proteins, termed long noncoding RNAs (lncRNAs), provides a new perspective in understanding the centrality of RNA in gene regulation (Rinn and Chang, 2012). Evidences have shown that lncRNAs are key regulators for many cellular functions, including splicing, gene regulation, and hormone-like activity (Gao et al, 2019a;Mongelli et al, 2019). Moreover, the dysregulation of lncRNAs has been proved to be closely related with various human diseases, such as types of cancer, neurological as well as cardiovascular diseases (Feng et al, 2018;Zhang et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction

Tan

Sun

et al. 2020

Front. Genet.

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Long noncoding RNAs (lncRNAs) are a class of noncoding RNA molecules longer than 200 nucleotides. Recent studies have uncovered their functional roles in diverse cellular processes and tumorigenesis. Therefore, identifying novel disease-related lncRNAs might deepen our understanding of disease etiology. However, due to the relatively small number of verified associations between lncRNAs and diseases, it remains a challenging task to reliably and effectively predict the associated lncRNAs for given diseases. In this paper, we propose a novel multiview consensus graph learning method to infer potential disease-related lncRNAs. Specifically, we first construct a set of similarity matrices for lncRNAs and diseases by taking advantage of the known associations. We then iteratively learn a consensus graph from the multiple input matrices and simultaneously optimize the predicted association probability based on a multi-label learning framework. To convey the utility of our method, three state-of-the-art methods are compared with our method on three widely used datasets. The experiment results illustrate that our method could obtain the best prediction performance under different cross validation schemes. The case study analysis implemented for uterine cervical neoplasms further confirmed the utility of our method in identifying lncRNAs as potential prognostic biomarkers in practice.

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Dual-network sparse graph regularized matrix factorization for predicting miRNA–disease associations

Abstract: Combined dual network, L2,1-norm and graph regularized matrix factorization for predicting miRNA–disease associations.

Cited by 23 publications

References 28 publications

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction

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