MGRCDA: Metagraph Recommendation Method for Predicting CircRNA–Disease Association

IEEE Transactions on Cybernetics, Год журнала: 2021, Номер 53(1), С. 67 - 75

Опубликована: Июль 8, 2021

Clinical evidence began to accumulate, suggesting that circRNAs can be novel therapeutic targets for various diseases and play a critical role in human health. However, limited by the complex mechanism of circRNA, it is difficult quickly large-scale explore relationship between disease circRNA wet-lab experiment. In this work, we design new computational model MGRCDA on account metagraph recommendation theory predict potential circRNA-disease associations. Specifically, first regard association prediction problem as system problem, series metagraphs according heterogeneous biological networks; then extract semantic information Gaussian interaction profile kernel (GIPK) similarity network attributes; finally, iterative search algorithm used calculate scores pair. On gold standard dataset circR2Disease, achieved accuracy 92.49% with an area under ROC curve 0.9298, which significantly higher than other state-of-the-art models. Furthermore, among top 30 disease-related recommended model, 25 have been verified latest published literature. The experimental results prove feasible efficient, recommend reliable candidates further experiment reduce scope

Язык: Английский

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Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 27(6), С. 3072 - 3082

Опубликована: Март 23, 2023

Exploring the relationship between circular RNA (circRNA) and disease is beneficial for revealing mechanisms of pathogenesis. However, a blind search all possible associations circRNAs diseases through biological experiments time-consuming. Although some prediction methods have been proposed, they still limitations. In this study, novel computational framework, called GATCL2CD, proposed to forecast unknown circRNA-disease (CDAs). First, we calculate Gaussian interactive profile kernel (GIP) similarity semantic diseases, circRNA sequence function similarity, GIPs circRNAs. Then, combine them construct heterogeneous graph. Thereafter, GATCL2CD proposes feature convolution learning that uses multi-head dynamic attention mechanism obtain different aggregated representations features correspond nodes in it extracts rich higher-order from stacked each node by using single-layer convolutional neural network with filter kernels sizes. Finally, pairwise element-wise product operation implemented capture interactions representations, multilayer perceptron introduced as an efficient classifier inferring potential CDAs. Major experimental results under 5-fold cross-validation (5-fold CV) on three datasets show superior five other state-of-the-art methods. Furthermore, case studies demonstrate suitability useful tool identifying disease-related

Язык: Английский

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A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation

BMC Biology, Год журнала: 2024, Номер 22(1)

Опубликована: Янв. 29, 2024

Abstract Background Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring relationship between circRNAs diseases is far-reaching significance for studying etiopathogenesis treating To this end, based on graph Markov neural network algorithm (GMNN) constructed our previous work GMNN2CD, we further considered multisource biological data that affects association circRNA disease developed an updated web server CircDA human hepatocellular carcinoma (HCC) tissue verify prediction results CircDA. Results built Tumarkov-based deep learning framework. The regards biomolecules as nodes interactions molecules edges, reasonably abstracts multiomics data, models them heterogeneous biomolecular network, which can reflect complex different biomolecules. Case studies using literature from HCC, cervical, gastric cancers demonstrate predictor identify missing associations known diseases, quantitative real-time PCR (RT-qPCR) experiment HCC samples, it was found five were significantly differentially expressed, proved predict related new circRNAs. Conclusions This efficient computational case analysis with sufficient feedback allows us circRNA-associated disease-associated Our provides method provide guidance certain For ease use, online ( http://server.malab.cn/CircDA ) provided, code open-sourced https://github.com/nmt315320/CircDA.git convenience improvement.

Язык: Английский

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metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge Learning

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

In the emerging field of RNA drugs, circular (circRNA) has attracted much attention as a novel multifunctional therapeutic target. Delving deeper into intricate interactions between circRNA and disease is critical for driving drug discovery efforts centered around circRNAs. Current computational methods face two significant limitations: lack aggregate information in heterogeneous graph networks higher-order fusion information. To this end, we present approach, metaCDA, which utilizes meta-knowledge adaptive learning to improve accuracy association predictions addresses limitations both. We calculate multiple similarity measures circRNA, construct based on these, apply meta-networks extract from graph, so that constructed maps have contrast enhancement Then, nodal aggregation system, integrates multihead mechanism mechanism, achieve accurate capture conducted extensive experiments, results show metaCDA outperforms existing state-of-the-art models can effectively predict disease-associated opening up new prospects circRNA-driven discovery.

Язык: Английский

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Predicting CircRNA-Disease Associations Through Linear Neighborhood Label Propagation Method

IEEE Access, Год журнала: 2019, Номер 7, С. 83474 - 83483

Опубликована: Янв. 1, 2019

Identification of circRNA-disease associations provides insight into the mechanism that circRNAs cause diseases. Wet experimental identification is time-consuming and labor-intensive, thus developing computational methods for association prediction an urgent task. In this paper, we propose a linear neighborhood label propagation method to predict associations, named CD-LNLP. First, CD-LNLP uses profiles based on known calculate circRNA-circRNA similarities disease-disease similarities. Next, implements similarity-based graph respectively associations. Finally, combine outputs from model produce results. experiments, achieves impressive performance with AUPR score 0.4487 AUC 0.9007 outperforms outstanding baseline (collaborative filter method, KATZ, nonnegative matrix factorization resource allocation method) state-of-the-art MRLDC. The case studies show identifies novel which are validated by up-to-date databases literature respectively. conclusion, promising predicting

Язык: Английский

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Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association

Scientific Reports, Год журнала: 2020, Номер 10(1)

Опубликована: Фев. 6, 2020

Abstract CircRNA is a special type of non-coding RNA, which closely related to the occurrence and development many complex human diseases. However, it time-consuming expensive determine circRNA-disease associations through experimental methods. Therefore, based on existing databases, we propose method named RWRKNN, integrates random walk with restart (RWR) k-nearest neighbors (KNN) predict between circRNAs Specifically, apply RWR algorithm weighting features global network topology information, employ KNN classify features. Finally, prediction scores each pair are obtained. As demonstrated by leave-one-out, 5-fold cross-validation 10-fold cross-validation, RWRKNN achieves AUC values 0.9297, 0.9333 0.9261, respectively. And case studies show that predicted can be successfully demonstrated. In conclusion, useful for predicting associations.

Язык: Английский

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A comprehensive survey on computational methods of non-coding RNA and disease association prediction

Briefings in Bioinformatics, Год журнала: 2020, Номер 22(4)

Опубликована: Ноя. 2, 2020

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods technologies producing biological data have also been developed. However, due to their high labor cost production time, nowadays, calculation-based methods, especially machine learning deep received a lot attention used commonly solve these problems. From computational point view, this survey mainly introduces three common RNAs, i.e. miRNAs, lncRNAs circRNAs, the related for predicting association with diseases. First, mainstream databases above introduced detail. Then, we present several RNA similarity disease calculations. Later, investigate ncRNA-disease prediction details classify into five types: network propagating, recommend system, matrix completion, learning. Furthermore, provide summary applications types associations respectively. Finally, advantages limitations various identified, future researches challenges discussed.

Язык: Английский

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Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning

IEEE/ACM Transactions on Computational Biology and Bioinformatics, Год журнала: 2020, Номер 19(1), С. 168 - 179

Опубликована: Апрель 16, 2020

A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve therapeutic patients, but negative cause major adverse reactions and even result in withdrawal from market patient death. Therefore, identifying has become key component development disease treatment. In this study, we propose novel method to predict based on integrated similarity semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates chemical, biological phenotype data calculate feature with cosine method. The Gaussian Interaction Profile kernel also calculated known DDIs. (the Regularized Least Squares classifier) used possibility scores pairs. terms 5-fold cross validation, 10-fold validation de novo achieve better prediction performance than other comparative methods. addition, average computation time shorter that Finally, case studies further demonstrate practical applications.

Язык: Английский

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SGANRDA: semi-supervised generative adversarial networks for predicting circRNA–disease associations

Briefings in Bioinformatics, Год журнала: 2021, Номер 22(5)

Опубликована: Март 16, 2021

Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, computational method fusing multi-source data to identify association between circRNA disease can effectively reduce cost save time. Considering limitations existing models, we propose semi-supervised generative adversarial network (GAN) model SGANRDA for predicting circRNA-disease association. This first fused natural language features sequence semantics, Gaussian interaction profile kernel, then used all pairs pre-train GAN network, fine-tune parameters through labeled samples. Finally, extreme learning machine classifier is employed obtain prediction result. previous supervision model, innovatively introduced sequences utilized information during pre-training process. step increase content feature some extent impact too few known associations on performance. obtained AUC scores 0.9411 0.9223 leave-one-out cross-validation 5-fold cross-validation, respectively. Prediction results benchmark dataset show outperforms other models. In addition, 25 top 30 highest case studies were verified by recent literature. These experimental demonstrate useful predict provide reliable candidates experiments.

Язык: Английский

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CircR2Disease v2.0: An Updated Web Server for Experimentally Validated circRNA–Disease Associations and Its Application

Genomics Proteomics & Bioinformatics, Год журнала: 2021, Номер 20(3), С. 435 - 445

Опубликована: Ноя. 29, 2021

Abstract With accumulating dysregulated circular RNAs (circRNAs) in pathological processes, the regulatory functions of circRNAs, especially circRNAs as microRNA (miRNA) sponges and their interactions with RNA-binding proteins (RBPs), have been widely validated. However, collected information on experimentally validated circRNA–disease associations is only preliminary. Therefore, an updated CircR2Disease database providing a comprehensive resource web tool to clarify relationships between diseases diverse species necessary. Here, we present v2.0 increased number novel characteristics. provides more than 5-fold compared its previous version. This version includes 4201 entries 3077 312 disease subtypes. Secondly, circRNA–miRNA, circRNA–miRNA–target, circRNA–RBP has manually for various diseases. Thirdly, gene symbols name IDs can be linked nomenclature databases. Detailed descriptions such samples journals also integrated into Thus, serve platform users systematically investigate roles further explore posttranscriptional function Finally, propose computational method named circDis based graph convolutional network (GCN) gradient boosting decision tree (GBDT) illustrate applications database. available at http://bioinfo.snnu.edu.cn/CircR2Disease_v2.0 https://github.com/bioinforlab/CircR2Disease-v2.0.

Язык: Английский

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