# Classification of Long Noncoding RNA Elements Using Deep Convolutional Neural Networks and Siamese Networks

arXiv: https://arxiv.org/abs/2102.05582 · HTML: https://mlsanity.com/paper/2102.05582/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Learning to Compare Image Patches via Convolutional Neural Networks](https://mlsanity.com/paper/1504.03641/) (2015)
- [A Twofold Siamese Network for Real-Time Object Tracking](https://mlsanity.com/paper/1802.08817/) (2018)
- [Siamese Instance Search for Tracking](https://mlsanity.com/paper/1605.05863/) (2016)
- [MDFN: Multi-Scale Deep Feature Learning Network for Object Detection](https://mlsanity.com/paper/1912.04514/) (2019)
- [ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images](https://mlsanity.com/paper/2002.12256/) (2020)
- [Adversarially Approximated Autoencoder for Image Generation and Manipulation](https://mlsanity.com/paper/1902.05581/) (2019)
- [A Comparative Study on Polyp Classification using Convolutional Neural Networks](https://mlsanity.com/paper/2007.06071/) (2020)
- [An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN](https://mlsanity.com/paper/1809.01263/) (2018)
- [Boosting Occluded Image Classification via Subspace Decomposition Based Estimation of Deep Features](https://mlsanity.com/paper/2001.04066/) (2020)
- [Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2008.10580/) (2020)

## Built on by (most cited first, 2 of 2)

- [Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques](https://mlsanity.com/paper/2401.13197/) (2024)
- [Assessing Patient Eligibility for Inspire Therapy through Machine Learning and Deep Learning Models](https://mlsanity.com/paper/2402.01067/) (2024)

## Abstract (arXiv)

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.