MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction

Chen, Xing; Yin, Jun; Qu, Jia; Huang, Li

doi:10.1371/journal.pcbi.1006418

Cited by 300 publications

(183 citation statements)

References 84 publications

(83 reference statements)

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“…Inspired by the successful application of disease semantic similarity in prioritizing reliable disease-associated ncRNAs, [31][32][33][34][35][36] we also capitalize on this similarity to enhance our predictions. As described in, 37 semantic similarities among diseases can be calculated according to their corresponding disease ontology, 38 which is organized as a directed acyclic graph.…”

Section: Disease Semantic Similaritymentioning

confidence: 99%

NCPCDA: network consistency projection for circRNA–disease association prediction

Yue

Liang

et al. 2019

RSC Adv.

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A growing body of evidence indicates that circular RNAs (circRNAs) play a pivotal role in various biological processes and have a close association with the initiation and progression of diseases. Moreover, circRNAs are considered as promising biomarkers for disease diagnosis owing to their characteristics of conservation, stability and universality. Inferring disease-circRNA relationships will contribute to the understanding of disease pathology. However, it is costly and laborious to discover novel disease-circRNA interactions by wet-lab experiments, and few computational methods have been devoted to predicting potential circRNAs for diseases. Here, we advance a computational method (NCPCDA) to identify novel circRNAdisease associations based on network consistency projection. For starters, we make use of multi-view similarity data, including circRNA functional similarity, disease semantic similarity, and association profile similarity, to construct the integrated circRNA similarity and disease similarity. Then, we project circRNA space and disease space on the circRNA-disease interaction network, respectively. Finally, we can obtain the predicted circRNA-disease association score matrix by combining the above two space projection scores. Simulation results show that NCPCDA can efficiently infer disease-circRNA relationships with high accuracy, obtaining AUCs of 0.9541 and 0.9201 in leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, case studies also suggest that NCPCDA is promising for discovering new disease-circRNA interactions. The NCPCDA dataset and code, as well as the detailed readme file for our code, can be downloaded from Github (https://github.com/ghli16/NNCPCD).

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Section: Disease Semantic Similaritymentioning

confidence: 99%

NCPCDA: network consistency projection for circRNA–disease association prediction

Yue

Liang

et al. 2019

RSC Adv.

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“…Gaussian interaction profile kernel similarity and Pearson correlation coefficient similarity of lncRNA and miRNA are calculated, respectively. After that, the functional similarity between lncRNA and lncRNA is defined according to [61,62], the final similarity matrix of lncRNA is calculated as follows:…”

Section: Construct the Overall Similarity For Lncrnas And Mirnas In mentioning

confidence: 99%

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Wang

You

et al. 2019

Preprint

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Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various biological processes. Emerging evidence suggests that the interactions between lncRNAs and miRNAs play an important role in regulating of genes and the development of many diseases. Due to the limited scale of known lncRNA-miRNA interactions, and expensive time and labor costs for identifying them by biological experiments, more accurate and efficient lncRNA-miRNA interactions computational prediction approach urgently need to be developed. In this work, we proposed a novel computational method, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. More specifically, the similarities both lncRNA and miRNA are calculated based on known interaction information and their sequence information. Then, the affinity graphs for lncRNAs and miRNAs are constructed using the p-nearest neighbors, respectively. Finally, a graph regularized nonnegative matrix factorization model is developed to accurately identify potential interactions between lncRNAs and miRNAs. To evaluate the performance of GNMFLMI, five-fold cross validation experiments are carried out. GNMFLMI achieves the AUC value of 0.9769 which outperforms the compared methods NMF and CNMF. In the case studies for lncRNA nonhsat159254.1 and miRNA hsa-mir-544a, 20 and 16 of the top-20 associations predicted by GNMFLMI are confirmed, respectively. Rigorous experimental results demonstrate that GNMFLMI can effectively predict novel lncRNA-miRNA interactions, which can provide guidance for relevant biomedical research.

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“…They introduced a preprocessing step, weighted k nearest neighbour (WKNKN) profiles, for both microRNAs and diseases, into GRNMF. Chen et al [49] designed an effective algorithm, MDHGI, according to matrix decomposition as well as a heterogeneous graph inference method for inferring potential miRNA-disease connections.…”

Section: Introductionmentioning

confidence: 99%

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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MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction

Cited by 300 publications

References 84 publications

NCPCDA: network consistency projection for circRNA–disease association prediction

NCPCDA: network consistency projection for circRNA–disease association prediction

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

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

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