Table 1.

List of deep learning methodologies in RNomics.

Tool	Approach	Target	Ref.
DeepTarget	Deep recurrent neural network-based auto-encoding and sequence–sequence interaction learning using expression data	miRNA–mRNA interactions	[140]
deepMirGene	Recurrent neural networks (RNNs), specifically long short-term memory (LSTM) networks using expression data	End-to-end learning approach that can identify precursor miRNAs	[141]
RPI-SAN	Auto-encoder neural networks	ncRNA–protein interaction pairs	[142]
DeepNets	Multilayer feed-forward artificial neural networks	RNA-Seq gene expression	[143]
eADAGE	Auto-encoder neural networks	Biological pathway enrichment from expression data	[144]
GCLMI	Graph convolution and auto-encoder	Potential lncRNA–miRNA interactions	[145]
RPITER	Convolution neural network (CNN) and stacked auto-encoder (SAE)	Prediction of ncRNA–protein interactions	[36]
DeePathology	Deep neural networks	Prediction of the origin of mRNA–miRNA interactions	[146]