2021
DOI: 10.3390/biomedicines9091152
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Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction

Abstract: Abnormal miRNA functions are widely involved in many diseases recorded in the database of experimentally supported human miRNA-disease associations (HMDD). Some of the associations are complicated: There can be up to five heterogeneous association types of miRNA with the same disease, including genetics type, epigenetics type, circulating miRNAs type, miRNA tissue expression type and miRNA-target interaction type. When one type of association is known for an miRNA-disease pair, it is important to predict any o… Show more

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Cited by 7 publications
(7 citation statements)
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“…Complex networks have attracted the attention of scholars in the fields of computer technology [1,2], biological information [3][4][5][6], physical science [7][8][9][10], social relations [11,12] and so on. Complex networks describe realworld networks as mathematical models.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Complex networks have attracted the attention of scholars in the fields of computer technology [1,2], biological information [3][4][5][6], physical science [7][8][9][10], social relations [11,12] and so on. Complex networks describe realworld networks as mathematical models.…”
Section: Introductionmentioning
confidence: 99%
“…As an important research object in the random walk process, the trapping problem [20] describes the physical phenomenon that wandering particles on the network are absorbed by the trap node. This problem is widely used in the capture of attackers on the Internet [1], the repair of paralyzed sites in the transportation network [21], the optimization of signal transmission in the communication network [2], the prediction and prevention of virus transmission [4,5] and so on. As an indicator of trap efficiency, the average trapping time (ATT) represents the dynamic exploration of a random walk on a network containing trap nodes, meaning the average sum of the time taken by all nodes in the network to reach the trap node.…”
Section: Introductionmentioning
confidence: 99%
“…Ha et al [ 32 ] designed a metric learning model to fuse heterogeneous features for predicting miRNA-disease associations. Yu et al [ 33 ] proposed a multi-layer heterogeneous network embedding model to predict potential miRNA-disease associations.…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning methods. Neural networks have been widely used for detecting potential associations among biological entities [ 28 , 33 , 34 ]. Zeng et al [ 35 ] adopted a neural network-based model to identify potential miRNA-disease associations.…”
Section: Introductionmentioning
confidence: 99%
“…(2) protein-related applications, such as protein-protein interactions (PPIs) (21)(22)(23)(24) and protein/gene disease interactions (25)(26)(27)(28)(29)(30)(31); and (3) transcriptomics-related applications, such as lncRNAs-diseases associations (32)(33)(34)(35) and miRNAdisease associations (36)(37)(38)(39)(40)(41)(42)(43) and many other applications (44)(45)(46)(47)(48)(49)(50). Since network embedding methods were not originally developed for biological networks, their performance in obtaining different biological network features is yet to be established.…”
Section: Introductionmentioning
confidence: 99%