Circ2Disease: a manually curated database of experimentally validated circRNAs in human disease

Yao, Dongxia; Zhang, Lei; Zheng, Mengyue; Sun, Xuan; Lu, Yan; Liu, Pengyuan

doi:10.1038/s41598-018-29360-3

Cited by 138 publications

(75 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, we added weights to each part of the integration similarity to see how the performance could be impacted. We added weights (range from 0 to 1) to Sim lev (circ i , circ j ) and Gkl(circ i , circ j ) in equation (8) and (9), respectively. For different weights circRNAs and diseases similarity, the final results were obtained by combining the two pairs.…”

Section: The Effect Of Adjusting Parameters On the Prediction Resultsmentioning

confidence: 99%

“…The Additional file 1: Figure S5 shows that the combinations of different similarity weights have similar results for the models obtained on different datasets. So, in the end, our model used equation (8) and (9) to respectively calculate the circRNAs similarity and diseases similarity.…”

Section: The Effect Of Adjusting Parameters On the Prediction Resultsmentioning

confidence: 99%

“…To date, an increasing number of experimentally verified or reported databases are available for the circRNA-disease associations, such as circR2Disease [7], circRNA disease [8], circ2Disease [9], and circ2Traits [10]. However, experimental methods are too expensive and time-consuming to obtain a large validated circRNAdisease association data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of circRNA-disease associations based on inductive matrix completion

Liu

Bin

et al. 2020

BMC Med Genomics

View full text Add to dashboard Cite

Background: Currently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time-and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies. Results: Here, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease associations. Based on known circRNA-disease associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under ROC curve (AUC) of 0.8465 with leave-one-out cross validation in the dataset, which is obtained by the combination of the three databases (circRNA disease, circ2Disease and circR2Disease). Our method surpasses other state-of-art models in predicting circRNA-disease associations. Furthermore, we conducted case studies in breast cancer, stomach cancer and colorectal cancer for further performance evaluation. Conclusion: All the results show reliable prediction ability of SIMCCDA. We anticipate that SIMCCDA could be utilized to facilitate further developments in the field and follow-up investigations by biomedical researchers.

show abstract

Section: The Effect Of Adjusting Parameters On the Prediction Resultsmentioning

confidence: 99%

Section: The Effect Of Adjusting Parameters On the Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of circRNA-disease associations based on inductive matrix completion

Liu

Bin

et al. 2020

BMC Med Genomics

View full text Add to dashboard Cite

show abstract

“…To construct the Molecular Association Network (MAN) comprehensively, 18 different kinds of experimental verified associations or interactions are collected from various databases [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. After the unifying identifier, we obtained a total of 8 diverse types of biomarkers.…”

Section: Construction Of the Molecular Association Network (Man)mentioning

confidence: 99%

Biomarker2vec: Attribute- and Behavior-driven Representation for Multi-type Relationship Prediction between Various Biomarkers

Guo

You

Wang

et al. 2019

Preprint

View full text Add to dashboard Cite

The explosive growth of genomic, chemical and pathological data provides new opportunities and challenges to re-recognize life activities within human cells. However, there exist few computational models that aggregate various biomarkers to comprehensively reveal the physical and functional landscape of the biology system. Here, we construct a graph called Molecular Association Network (MAN) and a representation method called Biomarker2vec. Specifically, MAN is a heterogeneous attribute network consists of 18 kinds of edges (relationships) among 8 kinds of nodes (biomarkers). Biomarker2vec is an algorithm that represents the nodes as vectors by integrating biomarker attribute and behavior. After the biomarkers are described as vectors, random forest classifier is applied to carry out the prediction task. Our approach achieved promising performance on 18 relationships, with AUC of 0.9608 and AUPR of 0.9572. We also empirically explored the contribution of attribute and behavior feature of biomarkers to the results. In addition, a drug-disease association prediction case study was performed to validate our method's ability on a specific object. These results strongly prove that MAN is a network with rich topological and biological information and Biomarker2vec can indeed adequately characterize biomarkers. Generally, our method can achieve simultaneous prediction of both single-type and multi-type relationships, which bring beneficial inspiration to relevant scholars and expand the medical research paradigm.

show abstract

“…As the number of detected circRNAs increases, multiple databases have been created to store information on circRNAs, such as Circ2Traits [20], circBase [21], deepBase [22] and CircNet [23]. Furthermore, researchers have gradually collected circRNA-disease associations supported by experiments and established databases, such as circR2Disease [24], circRNADb [25], circRNADisease [26] and Circ2Disease [27]. The accumulation of these data provides an opportunity for computational methods to predict potential circRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

GCNCDA: A New Method for Predicting CircRNA-Disease Associations Based on Graph Convolutional Network Algorithm

Wang

You

et al. 2019

Preprint

View full text Add to dashboard Cite

Numerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying the association between circRNAs and diseases plays a crucial role in exploring the pathogenesis of complex diseases and improving the diagnosis and treatment of diseases. However, due to the complex mechanisms between circRNAs and diseases, it is expensive and time-consuming to discover the new circRNA-disease associations by biological experiment. Therefore, there is increasingly urgent need for utilizing the computational methods to predict novel circRNA-disease associations. In this study, we propose a computational method called GCNCDA based on the deep learning Fast learning with Graph Convolutional Networks (FastGCN) algorithm to predict the potential disease-associated circRNAs. Specifically, the method first forms the unified descriptor by fusing disease semantic similarity information, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity information based on known circRNA-disease associations. The FastGCN algorithm is then used to objectively extract the high-level features contained in the fusion descriptor. Finally, the new circRNA-disease associations are accurately predicted by the Forest by Penalizing Attributes (Forest PA) classifier. The 5-fold cross-validation experiment of GCNCDA achieved 91.2% accuracy with 92.78% sensitivity at the AUC of 90.90% on circR2Disease benchmark dataset. In comparison with different classifier models, feature extraction models and other state-of-the-art methods, GCNCDA shows strong competitiveness. Furthermore, 10 of the top 15 circRNA-disease association candidates with the highest prediction scores were confirmed by recently published literature. These results suggest that GCNCDA can effectively predict potential circRNA-disease associations and provide highly credible candidates for biological experiments. Author SummaryThe recognition of circRNA-disease association is the key of disease diagnosis and treatment, and it is of great significance for exploring the pathogenesis of complex diseases. Computational methods can predicte the potential disease-related circRNAs quickly and accurately. Based on the hypothesis that circRNA with similar function tends to associate with similar disease, GCNCDA model is proposed to effectively predict the potential association between circRNAs and diseases by combining FastGCN algorithm. The performance of the model was verified by cross-validation experiments, different feature extraction algorithm and classifier models comparison experiments. Furthermore, 10 of the top 15 disease-associated circRNAs with the highest prediction scores were confirmed by recently published literature. It is anticipated that GCNCDA model can give priority to the most promising circRNA-disease associations on a large scale to provide reliable candidates for further biological experiment.

show abstract

Circ2Disease: a manually curated database of experimentally validated circRNAs in human disease

Cited by 138 publications

References 36 publications

Prediction of circRNA-disease associations based on inductive matrix completion

Prediction of circRNA-disease associations based on inductive matrix completion

Biomarker2vec: Attribute- and Behavior-driven Representation for Multi-type Relationship Prediction between Various Biomarkers

GCNCDA: A New Method for Predicting CircRNA-Disease Associations Based on Graph Convolutional Network Algorithm

Contact Info

Product

Resources

About