Inferring Potential CircRNA–Disease Associations via Deep Autoencoder-Based Classification

Deepthi, K.; Jereesh, A S

doi:10.1007/s40291-020-00499-y

Cited by 39 publications

(27 citation statements)

References 51 publications

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“…For the sake of rigor, we need to point out that since AE-RF [ 29 ] and ABHMDA [ 33 ] employ other similarity-based features besides the Gaussian interaction profile (GIP) kernel similarity. Considering the scarcity of relevant biological resources and convenience, we only calculated the GIP similarity for them in the experiments.…”

Section: Resultsmentioning

confidence: 99%

“…In a different way, SAEMDA [ 28 ] extracts features through semantic similarity. In terms of the prediction of circRNA-disease associations, AE-RF algorithm [ 29 ] also integrates many information sources to obtain the depth features. DMFCDA [ 30 ] constructed a circRNA-disease matrix with explicit and implicit feedback to capture the non-linear features.…”

Section: Introductionmentioning

confidence: 99%

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LRGCPND: Predicting Associations between ncRNA and Drug Resistance via Linear Residual Graph Convolution

Wang

Zhang

et al. 2021

IJMS

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Accurate inference of the relationship between non-coding RNAs (ncRNAs) and drug resistance is essential for understanding the complicated mechanisms of drug actions and clinical treatment. Traditional biological experiments are time-consuming, laborious, and minor in scale. Although several databases provide relevant resources, computational method for predicting this type of association has not yet been developed. In this paper, we leverage the verified association data of ncRNA and drug resistance to construct a bipartite graph and then develop a linear residual graph convolution approach for predicting associations between non-coding RNA and drug resistance (LRGCPND) without introducing or defining additional data. LRGCPND first aggregates the potential features of neighboring nodes per graph convolutional layer. Next, we transform the information between layers through a linear function. Eventually, LRGCPND unites the embedding representations of each layer to complete the prediction. Results of comparison experiments demonstrate that LRGCPND has more reliable performance than seven other state-of-the-art approaches with an average AUC value of 0.8987. Case studies illustrate that LRGCPND is an effective tool for inferring the associations between ncRNA and drug resistance.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LRGCPND: Predicting Associations between ncRNA and Drug Resistance via Linear Residual Graph Convolution

Wang

Zhang

et al. 2021

IJMS

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“…The GCMDR [ 36 ] is developed by Huang et al to predict the relationships between miRNAs and drugs, and GCN to be used by it for extraction feature and final scores calculation. The AE-RF [ 37 ] is developed by K. Deepthi et al to predict the associations between circRNAs and diseases, the Deep Auto-encoder (DAEN) algorithm is used by it to extract features and thereafter the Random Forest (RF) classifier is used to classify and predict the results of the score matrix. GCNMDA [ 38 ] is developed by Long et al to predict the associations between human micro-organisms and drugs, with a Conditional Random Fields (CRF) layer added to the GCN process for feature extraction and final scores calculation.…”

Section: Resultsmentioning

confidence: 99%

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Kuang

Deng

2021

BMC Bioinformatics

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Background The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. Results In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. Conclusions After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.

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“…Deepthi et al. [ 133 ] proposed an ensemble method of circRNA-disease association prediction based on a deep AntoEncoder and RF classifier (AE-RF) whose flow diagram is shown in Figure 16 . They first construct circRNA similarity matrix CS and disease similarity matrix DS by combing multiple types of similarity of circRNA and disease as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} CS\left({c}_i,{c}_j\right)=\left\{\begin{array}{@{}l} CFS\left({c}_i,{c}_j\right)\kern1em \mathrm{if}\kern0.2em {c}_i\kern0.3em \mathrm{and}\kern0.3em {c}_j\kern0.3em \mathrm{has}\kern0.3em \mathrm{functional}\kern0.3em \mathrm{simialrity}\\{} KC\left({c}_i,{c}_j\right)\kern1.6em \mathrm{otherwise}\end{array}\right.…”

Section: Computational Modelsmentioning

confidence: 99%

Circular RNAs and complex diseases: from experimental results to computational models

Wang

Han

Zhao

et al. 2021

Briefings in Bioinformatics

124

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Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.

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Inferring Potential CircRNA–Disease Associations via Deep Autoencoder-Based Classification

Cited by 39 publications

References 51 publications

LRGCPND: Predicting Associations between ncRNA and Drug Resistance via Linear Residual Graph Convolution

LRGCPND: Predicting Associations between ncRNA and Drug Resistance via Linear Residual Graph Convolution

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Circular RNAs and complex diseases: from experimental results to computational models

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