Fig 1.
The workflow of iCDA-CGR model to predict potential circRNA-disease associations.
Table 1.
Data distribution of the benchmark set circR2Disease and circFunBase of circRNA-disease association.
Fig 2.
A) the CGR of hsa_circ_0005931 are plotted with the average coordinates for each 8 × 8 quadrant represented. B) A matrix of hsa_circ_0005931’s nucleotides with probabilities for chaos game representation.
Fig 3.
The workflow of circRNA sequence-based similarity.
Fig 4.
ROC curves performed by iCDA-CGR on circR2Disease dataset.
Fig 5.
PR curves performed by iCDA-CGR on circR2Disease dataset.
Table 2.
The five-fold cross-validation results performed by iCDA-CGR on circR2Disease dataset.
Fig 6.
ROC curves performed by iCDA-CGR on circFunBase dataset.
Fig 7.
PR curves performed by iCDA-CGR on circFunBase dataset.
Table 3.
The five-fold cross-validation results performed by iCDA-CGR on circFunBase dataset.
Fig 8.
The ROCs of four different classifiers which are support vector machines, decision tree, random forest and k-nearest neighbor on circR2Disease dataset.
Table 4.
Performance comparison among four different classifiers which are k-nearest neighbor, random forest, decision tree and support vector machine.
Table 5.
Performance comparison (AUC scores) among four different prediction model which are iCDA-CGR, KATZHCDA, GHICD, RWRHCD and CD-LNLP, ICFCDA.
Table 6.
Prediction of the top 30 predicted circRNAs associated based on known associations on circR2Disease.
Table 7.
Prediction of the top 30 predicted circRNAs associated based on known associations on circFunBase.
Table 8.
Predictive results of the iCDA-CGR on other three databases.