Direct Inference of Base-Pairing Probabilities with Neural Networks Improves Prediction of RNA Secondary Structures with Pseudoknots

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Existing approaches to predicting RNA secondary structures depend on how the secondary structure is decomposed into substructures, that is, the architecture, to define their parameter space. However, architecture dependency has not been sufficiently investigated, especially for pseudoknotted secondary structures. In this study, we propose a novel algorithm for directly inferring base-pairing probabilities with neural networks that do not depend on the architecture of RNA secondary structures, and then implement this approach using two maximum expected accuracy (MEA)-based decoding algorithms: Nussinov-style decoding for pseudoknot-free structures and IPknot-style decoding for pseudoknotted structures. To train the neural networks connected to each base pair, we adopt a max-margin framework, called structured support vector machines (SSVM), as the output layer. Our benchmarks for predicting RNA secondary structures with and without pseudoknots show that our algorithm outperforms existing methods in prediction accuracy.

Original languageEnglish
Article number2155
JournalGenes
Volume13
Issue number11
DOIs
Publication statusPublished - 2022 Nov 18

Keywords

  • RNA secondary structure
  • deep learning
  • pseudoknots

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Fingerprint

Dive into the research topics of 'Direct Inference of Base-Pairing Probabilities with Neural Networks Improves Prediction of RNA Secondary Structures with Pseudoknots'. Together they form a unique fingerprint.

Cite this