Classification of Long Noncoding RNA Elements Using Deep Convolutional Neural Networks and Siamese Networks
This work addresses the classification of noncoding RNA for bioinformatics researchers, representing an incremental improvement by applying existing deep learning methods to a new domain-specific dataset.
The paper tackled the problem of classifying long noncoding RNA sequences by converting them into images based on base-pairing probability and using deep convolutional neural networks and Siamese networks for classification, achieving superior performance and efficiency compared to implemented models.
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.