Fig 1.
The overall framework of CRBPDL.
(A) The workflow of the development and assessment process of CRBPDL. (B) The structure of the sig-CRBPDL framework, including the input layer, convolutional layers, merger layers, inception module, attention layers, fully connected layers and output layer.
Fig 2.
(A) Comparison of model performance between different network depths visualized by box and fiddle charts. (B) Model performance analysis under different EPOCH. (C) Comparison of model performance under different learning rate schemes. (D) Comparison of model performance under different feature coding schemes.
Fig 3.
(A) Performance comparison of five feature codes.(B) Heat maps of different network model performance under 37 data sets.(C) T -SNE scatter plot with original feature coding. (D) T-SNE Scatter Diagram of Deep Feature after Deep Convolutional Network.
Fig 4.
(A) Performance comparison of MSRN and BiGRU. (B) The performance comparison between CRBPDL integrated model and various classification algorithms (C) ROC curves of 37 datasets under the integration model (D) Radar chart of ACC indicators of CRBPDL model under 31 lncRNA datasets.
Table 1.
Comparison of prediction performance under different classification models on 37 circRNA datasets.
Table 2.
Comparison of prediction performance under different classification models on 31 linear RNA datasets.