Abstract
Emerging evidence indicates that circRNA can regulate various diseases. However, the mechanisms of circRNA in these diseases have not been fully understood. Therefore, detecting potential circRNA–disease associations has far-reaching significance for pathological development and treatment of these diseases. In recent years, deep learning models are used in association analysis of circRNA–disease, but a lack of circRNA–disease association data limits further improvement. Therefore, there is an urgent need to mine more semantic information from data. In this paper, we propose a novel method called Semantic Association Analysis by Embedding and Deep learning (SAAED), which consists of two parts, a neural network embedding model called Entity Relation Network (ERN) and a Pseudo-Siamese network (PSN) for analysis. ERN can fuse multiple sources of data and express the information with low-dimensional embedding vectors. PSN can extract the feature between circRNA and disease for the association analysis. CircRNA–disease, circRNA–miRNA, disease–gene, disease–miRNA, disease–lncRNA, and disease–drug association information are used in this paper. More association data can be introduced for analysis without restriction. Based on the CircR2Disease benchmark dataset for evaluation, a fivefold cross-validation experiment showed an AUC of 98.92%, an accuracy of 95.39%, and a sensitivity of 93.06%. Compared with other state-of-the-art models, SAAED achieves the best overall performance. SAAED can expand the expression of the biological related information and is an efficient method for predicting potential circRNA–disease association.
Highlights
CircRNA is a non-coding RNA formed by reverse splicing (Nigro et al, 1991; Danan et al, 2012; Salzman et al, 2013) and performs multiple functions in the nucleus, cytoplasm, and extracellular matrix (Li et al, 2018)
To evaluate the performance of SAAED, we used the fivefold cross-validation to divide the data into training sets and testing sets in the ratio of 4:1, i.e., 1,000 data are used for training and 250 data are used for testing
We proposed a method called SAAED to calculate embedding vectors of circRNA and diseases to predict associations
Summary
CircRNA is a non-coding RNA formed by reverse splicing (Nigro et al, 1991; Danan et al, 2012; Salzman et al, 2013) and performs multiple functions in the nucleus, cytoplasm, and extracellular matrix (Li et al, 2018). CircRNA can regulate the splicing of their linear mRNA counterpart (Ashwal-Fluss et al, 2014; Zhang et al, 2014; Kelly et al, 2015) and control the transcription of parental genes (Li et al, 2015). As miRNA sponges (Hansen et al, 2013) and ceRNAs (Salmena et al, 2011), circRNAs competitively bind with miRNA, which can interact with target mRNAs to induce mRNA degradation and translational repression It has been proved that circMRPS35 governs histone modification in anticancer treatment and advocates for triggering the circMRPS35/KAT7/FOXO1/3a pathway to combat gastric cancer (Jie et al, 2020)
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