RNA secondary structure prediction with convolutional neural networks.

ABSTRACT:

Background

Predicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology. First-principle algorithmic approaches to this task are challenging because existing models of the folding process are inaccurate, and even if a perfect model existed, finding an optimal solution would be in general NP-complete.

Results

In this paper, we propose a simple, yet effective data-driven approach. We represent RNA sequences in the form of three-dimensional tensors in which we encode possible relations between all pairs of bases in a given sequence. We then use a convolutional neural network to predict a two-dimensional map which represents the correct pairings between the bases. Our model achieves significant accura

SUBMITTER: Saman Booy M 

PROVIDER: S-EPMC8812003 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature