# ncRNA Classification with Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1905.06515 · HTML: https://mlsanity.com/paper/1905.06515/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 8 of 8)

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- [Graph neural networks and attention-based CNN-LSTM for protein classification](https://mlsanity.com/paper/2204.09486/) (2022)
- [BSM: Small but Powerful Biological Sequence Model for Genes and Proteins](https://mlsanity.com/paper/2410.11499/) (2024)
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## Abstract (arXiv)

Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions. The task of ncRNA classification consists in classifying a given ncRNA sequence into its family. While it has been shown that the graph structure of an ncRNA sequence folding is of great importance for the prediction of its family, current methods make use of machine learning classifiers on hand-crafted graph features. We improve on the state-of-the-art for this task with a graph convolutional network model which achieves an accuracy of 85.73% and an F1-score of 85.61% over 13 classes. Moreover, our model learns in an end-to-end fashion from the raw RNA graphs and removes the need for expensive feature extraction. To the best of our knowledge, this also represents the first successful application of graph convolutional networks to RNA folding data.