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

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.

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PMDFI: Predicting miRNA–Disease Associations Based on High-Order Feature Interaction

Frontiers in Genetics ◽

10.3389/fgene.2021.656107 ◽

2021 ◽

Vol 12 ◽

Author(s):

Mingyan Tang ◽

Chenzhe Liu ◽

Dayun Liu ◽

Junyi Liu ◽

Jiaqi Liu ◽

...

Keyword(s):

Computational Models ◽

Treatment Method ◽

High Order ◽

Computational Method ◽

Feature Interaction ◽

Rna Molecules ◽

Feature Interactions ◽

Non Coding Rna ◽

Disease Associations ◽

Fold Cross Validation

MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.

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DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization

BMC Bioinformatics ◽

10.1186/s12859-020-03868-w ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Jin-Xing Liu ◽

Ming-Ming Gao ◽

Zhen Cui ◽

Ying-Lian Gao ◽

Feng Li

Keyword(s):

Matrix Factorization ◽

Factorization Method ◽

Treatment And Prevention ◽

Cancer Breast ◽

Disease Associations ◽

Network Similarity ◽

Huge Impact ◽

Auc Value ◽

Fold Cross Validation

Abstract Background In the development of science and technology, there are increasing evidences that there are some associations between lncRNAs and human diseases. Therefore, finding these associations between them will have a huge impact on our treatment and prevention of some diseases. However, the process of finding the associations between them is very difficult and requires a lot of time and effort. Therefore, it is particularly important to find some good methods for predicting lncRNA-disease associations (LDAs). Results In this paper, we propose a method based on dual sparse collaborative matrix factorization (DSCMF) to predict LDAs. The DSCMF method is improved on the traditional collaborative matrix factorization method. To increase the sparsity, the L2,1-norm is added in our method. At the same time, Gaussian interaction profile kernel is added to our method, which increase the network similarity between lncRNA and disease. Finally, the AUC value obtained by the experiment is used to evaluate the quality of our method, and the AUC value is obtained by the ten-fold cross-validation method. Conclusions The AUC value obtained by the DSCMF method is 0.8523. At the end of the paper, simulation experiment is carried out, and the experimental results of prostate cancer, breast cancer, ovarian cancer and colorectal cancer are analyzed in detail. The DSCMF method is expected to bring some help to lncRNA-disease associations research. The code can access the https://github.com/Ming-0113/DSCMF website.

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

International Journal of Molecular Sciences ◽

10.3390/ijms22168505 ◽

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.

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The Role of Circular RNAs in Keratinocyte Carcinomas

Cancers ◽

10.3390/cancers13164240 ◽

2021 ◽

Vol 13 (16) ◽

pp. 4240

Author(s):

Thomas Meyer ◽

Michael Sand ◽

Lutz Schmitz ◽

Eggert Stockfleth

Keyword(s):

Epithelial To Mesenchymal Transition ◽

Circular Rnas ◽

Mesenchymal Transition ◽

Rna Molecules ◽

Factors Associated ◽

New Therapeutic Approaches ◽

Non Coding Rna ◽

Micro Rnas ◽

Pubmed Search

Keratinocyte carcinomas (KC) include basal cell carcinomas (BCC) and cutaneous squamous cell carcinomas (cSCC) and represents the most common cancer in Europe and North America. Both entities are characterized by a very high mutational burden, mainly UV signature mutations. Predominately mutated genes in BCC belong to the sonic hedgehog pathway, whereas, in cSCC, TP53, CDKN2A, NOTCH1/2 and others are most frequently mutated. In addition, the dysregulation of factors associated with epithelial to mesenchymal transition (EMT) was shown in invasive cSCC. The expression of factors associated with tumorigenesis can be controlled in several ways and include non-coding RNA molecules, such as micro RNAs (miRNA) long noncoding RNAs (lncRNA) and circular RNAs (circRNA). To update findings on circRNA in KC, we reviewed 13 papers published since 2016, identified in a PubMed search. In both BCC and cSCC, numerous circRNAs were identified that were differently expressed compared to healthy skin. Some of them were shown to target miRNAs that are also dysregulated in KC. Moreover, some studies confirmed the biological functions of individual circRNAs involved in cancer development. Thus, circRNAs may be used as biomarkers of disease and disease progression and represent potential targets of new therapeutic approaches for KC.

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DNILMF-LDA: Prediction of lncRNA-Disease Associations by Dual-Network Integrated Logistic Matrix Factorization and Bayesian Optimization

Genes ◽

10.3390/genes10080608 ◽

2019 ◽

Vol 10 (8) ◽

pp. 608 ◽

Cited By ~ 3

Author(s):

Yan Li ◽

Junyi Li ◽

Naizheng Bian

Keyword(s):

Matrix Factorization ◽

Disease Diagnosis ◽

Disease Association ◽

Bayesian Optimization ◽

Model Parameters ◽

Target Interaction ◽

Disease Associations ◽

Auc Value ◽

Similarity Networks ◽

Fold Cross Validation

Identifying associations between lncRNAs and diseases can help understand disease-related lncRNAs and facilitate disease diagnosis and treatment. The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug–target interaction prediction, and good results have been achieved. We firstly applied DNILMF to lncRNA–disease association prediction (DNILMF-LDA). We combined different similarity kernel matrices of lncRNAs and diseases by using nonlinear fusion to extract the most important information in fused matrices. Then, lncRNA–disease association networks and similarity networks were built simultaneously. Finally, the Gaussian process mutual information (GP-MI) algorithm of Bayesian optimization was adopted to optimize the model parameters. The 10-fold cross-validation result showed that the area under receiving operating characteristic (ROC) curve (AUC) value of DNILMF-LDA was 0.9202, and the area under precision-recall (PR) curve (AUPR) was 0.5610. Compared with LRLSLDA, SIMCLDA, BiwalkLDA, and TPGLDA, the AUC value of our method increased by 38.81%, 13.07%, 8.35%, and 6.75%, respectively. The AUPR value of our method increased by 52.66%, 40.05%, 37.01%, and 44.25%. These results indicate that DNILMF-LDA is an effective method for predicting the associations between lncRNAs and diseases.

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MISIM v2.0: a web server for inferring microRNA functional similarity based on microRNA-disease associations

Nucleic Acids Research ◽

10.1093/nar/gkz328 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W536-W541 ◽

Cited By ~ 12

Author(s):

Jianwei Li ◽

Shan Zhang ◽

Yanping Wan ◽

Yingshu Zhao ◽

Jiangcheng Shi ◽

...

Keyword(s):

Web Server ◽

Enrichment Analysis ◽

Fold Increase ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Rna Molecules ◽

Novel Mirna ◽

Non Coding Rna ◽

Disease Associations ◽

Data Content

Abstract MicroRNAs (miRNAs) are one class of important small non-coding RNA molecules and play critical roles in health and disease. Therefore, it is important and necessary to evaluate the functional relationship of miRNAs and then predict novel miRNA-disease associations. For this purpose, here we developed the updated web server MISIM (miRNA similarity) v2.0. Besides a 3-fold increase in data content compared with MISIM v1.0, MISIM v2.0 improved the original MISIM algorithm by implementing both positive and negative miRNA-disease associations. That is, the MISIM v2.0 scores could be positive or negative, whereas MISIM v1.0 only produced positive scores. Moreover, MISIM v2.0 achieved an algorithm for novel miRNA-disease prediction based on MISIM v2.0 scores. Finally, MISIM v2.0 provided network visualization and functional enrichment analysis for functionally paired miRNAs. The MISIM v2.0 web server is freely accessible at http://www.lirmed.com/misim/.

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Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

BMC Bioinformatics ◽

10.1186/s12859-021-04486-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Feng Zhou ◽

Meng-Meng Yin ◽

Cui-Na Jiao ◽

Zhen Cui ◽

Jing-Xiu Zhao ◽

...

Keyword(s):

Bipartite Graph ◽

Matrix Factorization ◽

Cross Validation ◽

Rapid Development ◽

Factorization Method ◽

Computational Method ◽

Human Diseases ◽

Simulation Experiments ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

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Circular RNAs in cell differentiation and development

Development ◽

10.1242/dev.182725 ◽

2020 ◽

Vol 147 (16) ◽

pp. dev182725

Author(s):

Gaia Di Timoteo ◽

Francesca Rossi ◽

Irene Bozzoni

Keyword(s):

Gene Expression ◽

Cell Differentiation ◽

Gene Expression Regulation ◽

Current Knowledge ◽

Circular Rnas ◽

Additional Degree ◽

Rna Molecules ◽

Biological Features ◽

Circular Structure ◽

Eukaryotic Genomes

ABSTRACTIn recent years, circular RNAs (circRNAs) – a novel class of RNA molecules characterized by their covalently closed circular structure – have emerged as a complex family of eukaryotic transcripts with important biological features. Besides their peculiar structure, which makes them particularly stable molecules, they have attracted much interest because their expression is strongly tissue and cell specific. Moreover, many circRNAs are conserved across eukaryotes, localized in particular subcellular compartments, and can play disparate molecular functions. The discovery of circRNAs has therefore added not only another layer of gene expression regulation but also an additional degree of complexity to our understanding of the structure, function and evolution of eukaryotic genomes. In this Review, we summarize current knowledge of circRNAs and discuss the possible functions of circRNAs in cell differentiation and development.

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MicroRNAs, Long Non-Coding RNAs, and Circular RNAs: Potential Biomarkers and Therapeutic Targets in Pheochromocytoma/Paraganglioma

Cancers ◽

10.3390/cancers13071522 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1522

Author(s):

Peter Istvan Turai ◽

Gábor Nyírő ◽

Henriett Butz ◽

Attila Patócs ◽

Peter Igaz

Keyword(s):

Expression Profiles ◽

Treatment Options ◽

Tumor Biology ◽

Metastatic Potential ◽

Circular Rnas ◽

Ribonucleic Acids ◽

Rna Molecules ◽

Diagnostic Potential ◽

Non Coding Rna ◽

Non Coding Rnas

Around 40% of pheochromocytomas/paragangliomas (PPGL) harbor germline mutations, representing the highest heritability among human tumors. All PPGL have metastatic potential, but metastatic PPGL is overall rare. There is no available molecular marker for the metastatic potential of these tumors, and the diagnosis of metastatic PPGL can only be established if metastases are found at “extra-chromaffin” sites. In the era of precision medicine with individually targeted therapies and advanced care of patients, the treatment options for metastatic pheochromocytoma/paraganglioma are still limited. With this review we would like to nurture the idea of the quest for non-coding ribonucleic acids as an area to be further investigated in tumor biology. Non-coding RNA molecules encompassing microRNAs, long non-coding RNAs, and circular RNAs have been implicated in the pathogenesis of various tumors, and were also proposed as valuable diagnostic, prognostic factors, and even potential treatment targets. Given the fact that the pathogenesis of tumors including pheochromocytomas/paragangliomas is linked to epigenetic dysregulation, it is reasonable to conduct studies related to their epigenetic expression profiles and in this brief review we present a synopsis of currently available findings on the relevance of these molecules in these tumors highlighting their diagnostic potential.

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Annotation and curation of the causality information in LncRNADisease

Database ◽

10.1093/database/baz150 ◽

2020 ◽

Vol 2020 ◽

Author(s):

Kaiwen Jia ◽

Yuanxu Gao ◽

Jiangcheng Shi ◽

Yuan Zhou ◽

Yong Zhou ◽

...

Keyword(s):

Circular Rnas ◽

Non Coding Rna ◽

Disease Associations ◽

Non Coding Rnas ◽

Long Non Coding Rna ◽

Causality Information

Abstract Disease causative non-coding RNAs (ncRNAs) are of great importance in understanding a disease, for they directly contribute to the development or progress of a disease. Identifying the causative ncRNAs can provide vital implications for biomedical researches. In this work, we updated the long non-coding RNA disease database (LncRNADisease) with long non-coding RNA (lncRNA) causality information with manual annotations of the causal associations between lncRNAs/circular RNAs (circRNAs) and diseases by reviewing related publications. Of the total 11 568 experimental associations, 2297 out of 10 564 lncRNA-disease associations and 198 out of 1004 circRNA-disease associations were identified to be causal, whereas 635 lncRNAs and 126 circRNAs were identified to be causative for the development or progress of at least one disease. The updated information and functions of the database can offer great help to future researches involving lncRNA/circRNA-disease relationship. The latest LncRNADisease database is available at http://www.rnanut.net/lncrnadisease.

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