  In the last decade, the discovery of noncoding RNA(ncRNA) has exploded.
Classifying these ncRNA is critical todetermining their function. This thesis
proposes a new methodemploying deep convolutional neural networks (CNNs) to
classifyncRNA sequences. To this end, this paper first proposes anefficient
approach to convert the RNA sequences into imagescharacterizing their
base-pairing probability. As a result, clas-sifying RNA sequences is converted
to an image classificationproblem that can be efficiently solved by available
CNN-basedclassification models. This research also considers the
foldingpotential of the ncRNAs in addition to their primary sequence.Based on
the proposed approach, a benchmark image classifi-cation dataset is generated
from the RFAM database of ncRNAsequences. In addition, three classical CNN
models and threeSiamese network models have been implemented and comparedto
demonstrate the superior performance and efficiency of theproposed approach.
Extensive experimental results show thegreat potential of using deep learning
approaches for RNAclassification.
