A novel circRNA-miRNA association prediction model based on structural deep neural network embedding

Guo, Lu-Xiang; You, Zhu‐Hong; Wang, Lei; Yu, Chang-Qing; Zhao, Bo-Wei; Ren, Zhong-Hao; Pan, Jie

doi:10.1093/bib/bbac391

Cited by 31 publications

(19 citation statements)

References 38 publications

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“…WSCD builds a fusion feature model based on a convolutional neural network and deep neural network to predict CMI. 12 In addition, Table 6 shows the results of our comparison of KGDCMI, WSCD, SGCNCMI, and JSNDCMI with BCMCMI using the CMI-9905 dataset. Likewise, BCMCMI showed better stability and validity in predicting CMI.…”

Section: Comparison Of Different Classifiersmentioning

confidence: 99%

“…It uses the singular value decomposition algorithm to extract linear features from the matrix and predicts CMIs using the LightGBM classifier. In 2021, Guo et al introduced a novel model called WSCD, which integrates a convolutional neural network and deep neural network to acquire the hidden characteristics of nodes . The target’s attribute features are learned through Word2vec at first followed by the acquisition of underlying behavioral features using SDNE.…”

Section: Introductionmentioning

confidence: 99%

“…In 2021, Guo et al introduced a novel model called WSCD, which integrates a convolutional neural network and deep neural network to acquire the hidden characteristics of nodes. 12 The target's attribute features are learned through Word2vec at first followed by the acquisition of underlying behavioral features using SDNE. In 2022, Yao et al proposed the model IIMCCMA to predict CMIs through multivariate information fusion.…”

Section: Introductionmentioning

confidence: 99%

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BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path

Wei

et al. 2023

J. Chem. Inf. Model.

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More and more evidence suggests that circRNA plays a vital role in generating and treating diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA–miRNA interaction (CMI) has become urgent. However, traditional wet experiments are time-consuming and costly, and the results will be affected by objective factors. In this paper, we propose a computational model BCMCMI, which combines three features to predict CMI. Specifically, BCMCMI utilizes the bidirectional encoding capability of the BERT algorithm to extract sequence features from the semantic information of circRNA and miRNA. Then, a heterogeneous network is constructed based on cosine similarity and known CMI information. The Metapath2vec is employed to conduct random walks following meta-paths in the network to capture topological features, including similarity features. Finally, potential CMIs are predicted using the XGBoost classifier. BCMCMI achieves superior results compared to other state-of-the-art models on two benchmark datasets for CMI prediction. We also utilize t-SNE to visually observe the distribution of the extracted features on a randomly selected dataset. The remarkable prediction results show that BCMCMI can serve as a valuable complement to the wet experiment process.

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Section: Comparison Of Different Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path

Wei

et al. 2023

J. Chem. Inf. Model.

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“…A new sequence distributed representation learning-based method for potential LPI prediction, named LPI-Pred, was developed by Yi et al (2020a) which regarded lncRNA and protein sequences as "words" in natural language processing and trained the RNA2vec and Pro2vec models using word2vec. However, the above approach only considers information about the properties of lncRNAs and proteins and not their behavior, which has now been demonstrated in many articles in the field of bioinformatics to be powerful in improving the prediction of LPIs (Guan et al, 2022;Guo et al, 2022;Ren et al, 2022). Ge et al (2016) which proposed and tested a new computational approach, LPBNI, which constructs a bipartite network of lncRNA-proteins, using information about LPIs to link lncRNAs and proteins if they are known to interact with each other.…”

Section: Introductionmentioning

confidence: 99%

LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model

Wei

Yu²,

Li³

et al. 2023

Front. Genet.

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LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA–protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.

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“…Compared with traditional biological identification method, the interaction prediction model based on the computational method has higher accuracy and less time consumption. Guo et al (2022) presented a computational model to predict circRNA-miRNA interactions by using Word2vec, Structural Deep Network Embedding, Convolutional Neural Network, and Deep Neural Network. Qian et al (2022) proposed a computational model (CMASG) for circRNA-miRNA interactions prediction based on graph neural network and singular value decomposition.…”

Section: Introductionmentioning

confidence: 99%

Identifying circRNA-miRNA interaction based on multi-biological interaction fusion

Yao

Nong²,

Qin

et al. 2022

Front. Microbiol.

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CircRNA is a new type of non-coding RNA with a closed loop structure. More and more biological experiments show that circRNA plays important roles in many diseases by regulating the target genes of miRNA. Therefore, correct identification of the potential interaction between circRNA and miRNA not only helps to understand the mechanism of the disease, but also contributes to the diagnosis, treatment, and prognosis of the disease. In this study, we propose a model (IIMCCMA) by using network embedding and matrix completion to predict the potential interaction of circRNA-miRNA. Firstly, the corresponding adjacency matrix is constructed based on the experimentally verified circRNA-miRNA interaction, circRNA-cancer interaction, and miRNA-cancer interaction. Then, the Gaussian kernel function and the cosine function are used to calculate the circRNA Gaussian interaction profile kernel similarity, circRNA functional similarity, miRNA Gaussian interaction profile kernel similarity, and miRNA functional similarity. In order to reduce the influence of noise and redundant information in known interactions, this model uses network embedding to extract the potential feature vectors of circRNA and miRNA, respectively. Finally, an improved inductive matrix completion algorithm based on the feature vectors of circRNA and miRNA is used to identify potential interactions between circRNAs and miRNAs. The 10-fold cross-validation experiment is utilized to prove the predictive ability of the IIMCCMA. The experimental results show that the AUC value and AUPR value of the IIMCCMA model are higher than other state-of-the-art algorithms. In addition, case studies show that the IIMCCMA model can correctly identify the potential interactions between circRNAs and miRNAs.

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A novel circRNA-miRNA association prediction model based on structural deep neural network embedding

Cited by 31 publications

References 38 publications

BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path

BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path

LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model

Identifying circRNA-miRNA interaction based on multi-biological interaction fusion

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