scholarly journals GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations

2021 ◽  
Vol 22 (16) ◽  
pp. 8505
Author(s):  
Cunmei Ji ◽  
Zhihao Liu ◽  
Yutian Wang ◽  
Jiancheng Ni ◽  
Chunhou Zheng
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
State Of The Art ◽  
Computational Method ◽  
Circular Rnas ◽  
Attention Network ◽  
Initial Node ◽  
New Class ◽  
Disease Associations ◽  
Non Coding Rnas

Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, effective computational methods are an urgent need for predicting potential circRNA–disease associations. In this study, we proposed a novel computational method named GATNNCDA, which combines Graph Attention Network (GAT) and multi-layer neural network (NN) to infer disease-related circRNAs. Specially, GATNNCDA first integrates disease semantic similarity, circRNA functional similarity and the respective Gaussian Interaction Profile (GIP) kernel similarities. The integrated similarities are used as initial node features, and then GAT is applied for further feature extraction in the heterogeneous circRNA–disease graph. Finally, the NN-based classifier is introduced for prediction. The results of fivefold cross validation demonstrated that GATNNCDA achieved an average AUC of 0.9613 and AUPR of 0.9433 on the CircR2Disease dataset, and outperformed other state-of-the-art methods. In addition, case studies on breast cancer and hepatocellular carcinoma showed that 20 and 18 of the top 20 candidates were respectively confirmed in the validation datasets or published literature. Therefore, GATNNCDA is an effective and reliable tool for discovering circRNA–disease associations.

scholarly journals GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2595
Author(s):  
Chen Bian ◽  
Xiu-Juan Lei ◽  
Fang-Xiang Wu
Keyword(s):  
Computational Method ◽  
Circular Rnas ◽  
Attention Network ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Circular Structure ◽  
Disease Associations ◽  
Network Similarity ◽  
Auc Value ◽  
Fold Cross Validation

CircRNAs (circular RNAs) are a class of non-coding RNA molecules with a closed circular structure. CircRNAs are closely related to the occurrence and development of diseases. Due to the time-consuming nature of biological experiments, computational methods have become a better way to predict the interactions between circRNAs and diseases. In this study, we developed a novel computational method called GATCDA utilizing a graph attention network (GAT) to predict circRNA–disease associations with disease symptom similarity, network similarity, and information entropy similarity for both circRNAs and diseases. GAT learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. Considering that the circRNA–miRNA–mRNA axis plays an important role in the generation and development of diseases, circRNA–miRNA interactions and disease–mRNA interactions were adopted to construct features, in which mRNAs were related to 88% of miRNAs. As demonstrated by five-fold cross-validation, GATCDA yielded an AUC value of 0.9011. In addition, case studies showed that GATCDA can predict unknown circRNA–disease associations. In conclusion, GATCDA is a useful method for exploring associations between circRNAs and diseases.

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

2019 ◽  
Vol 21 (4) ◽  
pp. 1356-1367 ◽  
Author(s):  
Hang Wei ◽  
Bin Liu
Keyword(s):  
Matrix Factorization ◽  
Complex Disease ◽  
False Negative ◽  
Computational Method ◽  
Circular Rnas ◽  
Disease Mechanism ◽  
Disease Similarity ◽  
The Matrix ◽  
Disease Associations ◽  
Non Coding Rnas

Abstract Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

scholarly journals Circular RNAs: Potential Applications as Therapeutic Targets and Biomarkers in Breast Cancer

2021 ◽  
Vol 7 (1) ◽  
pp. 2
Author(s):  
Debina Sarkar ◽  
Sarah D. Diermeier
Keyword(s):  
Breast Cancer ◽  
Translational Regulation ◽  
Closed Loop ◽  
Mechanisms Of Action ◽  
Circular Rnas ◽  
Therapeutic Approaches ◽  
Mirna Sponge ◽  
Bodily Fluids ◽  
Potential Applications ◽  
Non Coding Rnas

Circular RNAs (circRNAs) are a class of non-coding RNAs that form a covalently closed loop. A number of functions and mechanisms of action for circRNAs have been reported, including as miRNA sponge, exerting transcriptional and translational regulation, interacting with proteins, and coding for peptides. CircRNA dysregulation has also been implicated in many cancers, such as breast cancer. Their relatively high stability and presence in bodily fluids makes cancer-associated circRNAs promising candidates as a new biomarker. In this review, we summarize the research undertaken on circRNAs associated with breast cancer, discuss circRNAs as biomarkers, and present circRNA-based therapeutic approaches.

scholarly journals Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases

2021 ◽  
Vol 12 ◽  
Author(s):  
Shihu Jiao ◽  
Song Wu ◽  
Shan Huang ◽  
Mingyang Liu ◽  
Bo Gao
Keyword(s):  
Neurological Disorders ◽  
Closed Loop ◽  
Significant Proportion ◽  
Human Diseases ◽  
Circular Rnas ◽  
The Status ◽  
Research Questions ◽  
Non Coding Rnas ◽  
Microrna Sponges

Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs (ncRNAs) with a closed-loop structure that are mainly produced by variable processing of precursor mRNAs (pre-mRNAs). They are widely present in all eukaryotes and are very stable. Currently, circRNA studies have become a hotspot in RNA research. It has been reported that circRNAs constitute a significant proportion of transcript expression, and some are significantly more abundantly expressed than other transcripts. CircRNAs have regulatory roles in gene expression and critical biological functions in the development of organisms, such as acting as microRNA sponges or as endogenous RNAs and biomarkers. As such, they may have useful functions in the diagnosis and treatment of diseases. CircRNAs have been found to play an important role in the development of several diseases, including atherosclerosis, neurological disorders, diabetes, and cancer. In this paper, we review the status of circRNA research, describe circRNA-related databases and the identification of circRNAs, discuss the role of circRNAs in human diseases such as colon cancer, atherosclerosis, and gastric cancer, and identify remaining research questions related to circRNAs.

scholarly journals EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments

2020 ◽  
Vol 49 (D1) ◽  
pp. D86-D91
Author(s):  
Bailing Zhou ◽  
Baohua Ji ◽  
Kui Liu ◽  
Guodong Hu ◽  
Fei Wang ◽  
...  
Keyword(s):  
Interaction Network ◽  
Circular Rnas ◽  
Biological Processes ◽  
Drug Resistant ◽  
Functional Roles ◽  
Visual Interaction ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Central Database ◽  
Data Browsing

Abstract Long non-coding RNAs (lncRNAs) play important functional roles in many diverse biological processes. However, not all expressed lncRNAs are functional. Thus, it is necessary to manually collect all experimentally validated functional lncRNAs (EVlncRNA) with their sequences, structures, and functions annotated in a central database. The first release of such a database (EVLncRNAs) was made using the literature prior to 1 May 2016. Since then (till 15 May 2020), 19 245 articles related to lncRNAs have been published. In EVLncRNAs 2.0, these articles were manually examined for a major expansion of the data collected. Specifically, the number of annotated EVlncRNAs, associated diseases, lncRNA-disease associations, and interaction records were increased by 260%, 320%, 484% and 537%, respectively. Moreover, the database has added several new categories: 8 lncRNA structures, 33 exosomal lncRNAs, 188 circular RNAs, and 1079 drug-resistant, chemoresistant, and stress-resistant lncRNAs. All records have checked against known retraction and fake articles. This release also comes with a highly interactive visual interaction network that facilitates users to track the underlying relations among lncRNAs, miRNAs, proteins, genes and other functional elements. Furthermore, it provides links to four new bioinformatics tools with improved data browsing and searching functionality. EVLncRNAs 2.0 is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs2/.

scholarly journals Circular RNAs and Hepatocellular Carcinoma: New Epigenetic Players With Diagnostic and Prognostic Roles

2021 ◽  
Vol 11 ◽  
Author(s):  
Kedeerya Aishanjiang ◽  
Xin-dong Wei ◽  
Yi Fu ◽  
Xinjie Lin ◽  
Yujie Ma ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Hepatocellular Carcinoma ◽  
Closed Loop ◽  
Therapeutic Potential ◽  
Therapeutic Targets ◽  
Circular Rnas ◽  
Diagnosis And Prognosis ◽  
Physiological Processes ◽  
Non Coding Rnas ◽  
Novel Targets

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Due to the lack of potent diagnosis and prognosis biomarkers and effective therapeutic targets, the overall prognosis of survival is poor in HCC patients. Circular RNAs (circRNAs) are a class of novel endogenous non-coding RNAs with covalently closed loop structures and implicated in diverse physiological processes and pathological diseases. Recent studies have demonstrated the involvement of circRNAs in HCC diagnosis, prognosis, development, and drug resistance, suggesting that circRNAs may be a class of novel targets for improving HCC diagnosis, prognosis, and treatments. In fact, some artificial circRNAs have been engineered and showed their therapeutic potential in treating HCV infection and gastric cancer. In this review, we introduce the potential of circRNAs as biomarkers for HCC diagnosis and prognosis, as therapeutic targets for HCC treatments and discuss the challenges in circRNA research and chances of circRNA application.

scholarly journals Circular RNAs in Hepatocellular Carcinoma: Emerging Functions to Clinical Significances

2021 ◽  
Vol 11 ◽  
Author(s):  
Yucheng Zhang ◽  
Yali Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Closed Loop ◽  
Early Stage ◽  
Circular Rnas ◽  
High Morbidity ◽  
Future Perspectives ◽  
Diagnostic Biomarkers ◽  
Diagnosis And Prognosis ◽  
Highly Sensitive ◽  
Non Coding Rnas

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver and carries high morbidity and mortality. Diagnosing HCC at an early stage is challenging. Therefore, finding new, highly sensitive and specific diagnostic biomarkers for the diagnosis and prognosis of HCC patients is extremely important. Circular RNAs (circRNAs) are a class of non-coding RNAs with covalently closed loop structures. They are characterized by remarkable stability, long half-life, abundance and evolutionary conservation. Recent studies have shown that many circRNAs are expressed aberrantly in HCC tissues and have important regulatory roles during the development and progression of HCC. Hence, circRNAs are promising biomarkers for the diagnosis and prognosis of HCC. This review: (i) summarizes the biogenesis, categories, and functions of circRNAs; (ii) focuses on current progress of dysregulated expression of circRNAs in HCC with regard to regulation of the tumor hallmarks, “stemness” of cancer cells, and immunotherapy; (iii) highlights circRNAs as potential biomarkers and therapeutic targets for HCC; and (iv) discusses some of the challenges, questions and future perspectives of circRNAs research in HCC.

scholarly journals A Novel Approach for Predicting Disease-lncRNA Associations Based on the Distance Correlation Set and Information of the miRNAs

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haochen Zhao ◽  
Linai Kuang ◽  
Lei Wang ◽  
Zhanwei Xuan
Keyword(s):  
Computational Models ◽  
State Of The Art ◽  
Experimental Studies ◽  
Research Field ◽  
Computational Method ◽  
Biological Processes ◽  
Distance Correlation ◽  
Novel Approach ◽  
Disease Associations ◽  
Leave One Out

Recently, accumulating laboratorial studies have indicated that plenty of long noncoding RNAs (lncRNAs) play important roles in various biological processes and are associated with many complex human diseases. Therefore, developing powerful computational models to predict correlation between lncRNAs and diseases based on heterogeneous biological datasets will be important. However, there are few approaches to calculating and analyzing lncRNA-disease associations on the basis of information about miRNAs. In this article, a new computational method based on distance correlation set is developed to predict lncRNA-disease associations (DCSLDA). Comparing with existing state-of-the-art methods, we found that the major novelty of DCSLDA lies in the introduction of lncRNA-miRNA-disease network and distance correlation set; thus DCSLDA can be applied to predict potential lncRNA-disease associations without requiring any known disease-lncRNA associations. Simulation results show that DCSLDA can significantly improve previous existing models with reliable AUC of 0.8517 in the leave-one-out cross-validation. Furthermore, while implementing DCSLDA to prioritize candidate lncRNAs for three important cancers, in the first 0.5% of forecast results, 17 predicted associations are verified by other independent studies and biological experimental studies. Hence, it is anticipated that DCSLDA could be a great addition to the biomedical research field.

scholarly journals Gated Graph Attention Network for Cancer Prediction

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1938
Author(s):  
Linling Qiu ◽  
Han Li ◽  
Meihong Wang ◽  
Xiaoli Wang
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
State Of The Art ◽  
Network Models ◽  
The State ◽  
Neural Network Models ◽  
Attention Network ◽  
Training Time ◽  
Cancer Prediction ◽  
Gating Mechanism

With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating mechanism (GM) is introduced to work with the attention mechanism (AM), to break through the previous work’s limitation of 1-hop neighbourhood reasoning. In this way, our GGAT is capable of fully mining the potential correlation between related samples, helping for improving the cancer prediction accuracy. Additionally, to simplify the datasets, we propose a hybrid feature selection algorithm to strictly select gene features, which significantly reduces training time without affecting prediction accuracy. To the best of our knowledge, our proposed GGAT achieves the state-of-the-art results in cancer prediction task on LIHC, LUAD, KIRC compared to other traditional machine learning methods and neural network models, and improves the accuracy by 1% to 2% on Cora dataset, compared to the state-of-the-art graph neural network methods.

scholarly journals Verisig 2.0: Verification of Neural Network Controllers Using Taylor Model Preconditioning

2021 ◽  
pp. 249-262
Author(s):  
Radoslav Ivanov ◽  
Taylor Carpenter ◽  
James Weimer ◽  
Rajeev Alur ◽  
George Pappas ◽  
...  
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
State Of The Art ◽  
Taylor Model ◽  
Shrink Wrapping ◽  
Closed Loop Systems ◽  
Verification Tool ◽  
Extensive Evaluation ◽  
Verification Tools ◽  
Neural Network Controllers

AbstractThis paper presents Verisig 2.0, a verification tool for closed-loop systems with neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop a Taylor-model-based reachability algorithm through Taylor model preconditioning and shrink wrapping. Furthermore, we provide a parallelized implementation that allows Verisig 2.0 to efficiently handle larger NNs than existing tools can. We provide an extensive evaluation over 10 benchmarks and compare Verisig 2.0 against three state-of-the-art verification tools. We show that Verisig 2.0 is both more accurate and faster, achieving speed-ups of up to 21x and 268x against different tools, respectively.

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