Ribonucleic acids (RNAs) involve in various physiological/pathological processes by interacting with proteins, compounds, and other RNAs. A variety of powerful computational methods have been developed to predict such valuable interactions. However, all these methods rely heavily on the ‘digitalization’ (also known as ‘encoding’) of RNA-associated interacting pairs into a computer-recognizable descriptor. In other words, it is urgently needed to have a powerful tool that can not only represent each interacting partner but also integrate both partners into a computer-recognizable interaction.
Researchers at Zhejiang University School of Medicine have developed RNAincoder (deep learning-based encoder for RNA-associated interactions) to (a) provide a comprehensive collection of RNA encoding features, (b) realize the representation of any RNA-associated interaction based on a well-established deep learning-based embedding strategy and (c) enable large-scale scanning of all possible feature combinations to identify the one of optimal performance in RNA-associated interaction prediction. The effectiveness of RNAincoder was extensively validated by case studies on benchmark datasets. All in all, RNAincoder is distinguished for its capability in providing a more accurate representation of RNA-associated interactions, which makes it an indispensable complement to other available tools.
(A) the deep learning-based embedding strategy for RNA-associated interactions and the framework of (B) the stacked autoencoder (SAE) in RNAincoder. The stacked autoencoder consisted of three autoencoders and each autoencoder included an encoder and a decoder based on a multilayer perceptron. Embedded features sequentially optimized by encoders in three pre-trained autoencoders would be paired and concatenated for the prediction of RNA-associated interactions.
Availability – RNAincoder can be accessed at https://idrblab.org/rnaincoder/.