A head-to-head zero-shot benchmark of 13 RNA language models finds that strong secondary-structure prediction often comes at the cost of poor functional classification performance.
ncRNA Classification with Graph Convolutional Networks
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abstract
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.
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A Comparative Review of RNA Language Models
A head-to-head zero-shot benchmark of 13 RNA language models finds that strong secondary-structure prediction often comes at the cost of poor functional classification performance.