Table 2.

The performance on classification pairwise ncRNAs

Methods	Accuracy	F-value
CNN with Clustal Omega (one-hot encoding)	0.9580	0.8500
CNN with DAFS (one-hot encoding)	0.9710	0.9010
CNN with DAFS (word2vec)	0.9800	0.9310
CNN with SSR, SIR and SAR	0.9923	0.9954
GCFM’s architecture with SSR	0.9945	0.9834
GCFM’s architecture with SIR	0.9076	0.9459
GCFM’s architecture with SAR	0.9589	0.9697
GCFM	0.9991	0.9973

Notes: ’CNN with Clustal Omega (one-hot encoding)’ represents the CNN model with the input of Clustal-Omega alignments and one-hot encoding representation, ’CNN with DAFS (one-hot encoding)’ represents the CNN model with the input of DAFS alignments and one-hot encoding representation, ’CNN with DAFS (word2vec)’ denotes the CNN model with the input of DAFS alignments and word2vec distributed representation, ’CNN with SSR, SIR and SAR’ represents the CNN model with the input of our multi-view structure representation, ’GCFM’s architecture with SSR,’ ’GCFM’s architecture with SIR’ and ’GCFM’s architecture SAR,’ respectively, denotes the model architecture of GCFM with the input of SSR, with the input of SIR and with the input of SAR, ”GCFM” represents deep ensembling learning architecture with the input of multi-view structure representation (i.e. SSR, SIR and SAR). Bold font is used to indicate the best performance.