  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.
