Abstract
miRNAs significantly affect multifarious biological processes involving human disease. Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA‐disease associations, a lot of high‐efficiency computational methods by computer have been developed, based on a network generated by miRNA‐disease association dataset. However, there exist many challenges. Firstly, the association between miRNAs and diseases is intricate. These methods should consider the influence of the neighborhoods of each node from the network. Secondly, how to measure whether there is an association between two nodes of the network is also an important problem. In our study, we innovatively integrate graph node embedding with a multilayer perceptron and propose a method DEMLP. To begin with, we construct a miRNA‐disease network by miRNA‐disease adjacency matrix (MDA). Then, low‐dimensional embedding representation vectors of nodes are learned from the miRNA‐disease network by DeepWalk. Finally, we use these low‐dimensional embedding representation vectors as input to train the multilayer perceptron. Experiments show that our proposed method that only utilized the miRNA–disease association information can effectively predict miRNA‐disease associations. To evaluate the effectiveness of DEMLP in a miRNA‐disease network from HMDD v3.2, we apply fivefold crossvalidation in our study. The ROC‐AUC computed result value of DEMLP is 0.943, and the PR‐AUC value of DEMLP is 0.937. Compared with other state‐of‐the‐art methods, our method shows good performance using only the miRNA‐disease interaction network.
Highlights
More and more evidence shows that miRNA impacts biological processes in a very significant way, like cell development and cell proliferation [1,2,3,4]
We compare our model with other four known stateof-the-art models, and the receiver operating characteristics (ROC) area under the curve (AUC) of our proposed method is 0.923, which is far more than any other model using the same data HMDD v2.0
Taking expense and difficulty into consideration, to predict the potential miRNA-disease associations, a lot of highefficiency computational methods by computer have been developed, based on a network generated by miRNA-disease association dataset
Summary
More and more evidence shows that miRNA impacts biological processes in a very significant way, like cell development and cell proliferation [1,2,3,4]. To predict the potential association of miRNA disease, a lot of models based on the known miRNA-disease association network have been developed These methods mainly consisted of two categories: the score function-based algorithms and machine learning. Differing from common local network similarity measures, a method named RWRMDA is generated by Chen et al (2012), which employ overall measurement of network similarity and adopt node embedding presentation method random walk to infer the potential association of miRNA disease [25]. By integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, a method named PBMDA is proposed by You et al (2017) This method constructs a heterogeneous graph consisting of three interlinked subgraphs and further adopts a depth first search algorithm to infer potential miRNA-disease associations [29]. We compare our model with other four known stateof-the-art models, and the receiver operating characteristics (ROC) area under the curve (AUC) of our proposed method is 0.923, which is far more than any other model using the same data HMDD v2.0
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
