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CircNet: an encoder–decoder-based convolution neural network (CNN) for circular RNA identification

  • S.I. : Healthcare Analytics
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Abstract

Discrimination of circular RNA from long non-coding RNA is important to understand its role in different biological processes, disease prediction and cure. Identifying circular RNA through manual laboratories work is expensive, time-consuming and prone to errors. Development of computational methodologies for identification of circular RNA is an active area of research. State-of-the-art circular RNA identification methodologies make use of handcrafted features, which not only increase the feature space, but also extract irrelevant and redundant features. The paper in hand proposes an end-to-end deep learning-based framework named as CircNet, which does not require any handcrafted features. It takes raw RNA sequence as an input and utilises encoder–decoder based convolutional operations to learn lower-dimensional latent representation. This latent representation is further passed to another convolutional architecture to extract discriminative features followed by a classification layer. We performed extensive experimentation to highlight different regions of genome sequence that preserve the most important information for identifying circular RNAs. CircNet significantly outperforms state-of-the-art approaches with a considerable margin 10.29% in terms F1 measure.

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Funding

Sartorius Artificial Intelligence Lab.

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Authors and Affiliations

  1. German Research Center for Artificial Intelligence (DFKI), 67663, Kaiserslautern, Germany

    Marco Stricker, Muhammad Nabeel Asim, Andreas Dengel & Sheraz Ahmed

  2. TU Kaiserslautern, 67663, Kaiserslautern, Germany

    Marco Stricker, Muhammad Nabeel Asim & Andreas Dengel

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  1. Marco Stricker
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  2. Muhammad Nabeel Asim
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Correspondence to Muhammad Nabeel Asim.

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Stricker, M., Asim, M.N., Dengel, A. et al. CircNet: an encoder–decoder-based convolution neural network (CNN) for circular RNA identification. Neural Comput & Applic 34, 11441–11452 (2022). https://doi.org/10.1007/s00521-020-05673-1

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