ArticlePLoS computational biology2022
Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- Conserved 3' stem-loop structures enable comprehensive analysis of bacterial transcription termination in metagenomes.Microbiome · 2026Article
- Canonical transcription termination mechanisms explain a minority of operons in cyanobacteria.mSystems · 2026Article
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- TerminatorNet: comprehensive identification of intrinsic transcription terminators in bacteria.Bioinformatics (Oxford, England) · 2026Article
- DNATCO v5.0: integrated web platform for 3D nucleic acid structure analysis.Nucleic acids research · 2026Article
- Canonical transcription termination mechanisms explain a minority of operons in cyanobacteria.bioRxiv : the preprint server for biology · 2025Article
- BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble.NAR genomics and bioinformatics · 2025Article
- A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data.Computational and structural biotechnology journal · 2025Article
- TSS-Captur: a user-friendly pipeline for characterizing unclassified RNA transcripts.NAR genomics and bioinformatics · 2024Article
- Machine Learning to Enhance Biophysical Models for Riboswitch Discovery.Wiley interdisciplinary reviews. RNAReview
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is well-established that neural networks can predict or identify structural motifs of non-coding RNAs (ncRNAs). Yet, the neural network based identification of RNA structural motifs is limited by the availability of training data that are often insufficient for learning features of specific ncRNA families or structural motifs. Aiming to reliably identify intrinsic transcription terminators in bacteria, we introduce a novel pre-training approach that uses inverse folding to generate training data for predicting or identifying a specific family or structural motif of ncRNA. We assess the ability of neural networks to identify secondary structure by systematic in silico mutagenesis experiments. In a study to identify intrinsic transcription terminators as functionally well-understood RNA structural motifs, our inverse folding based pre-training approach significantly boosts the performance of neural network topologies, which outperform previous approaches to identify intrinsic transcription terminators. Inverse-folding based pre-training provides a simple, yet highly effective way to integrate the well-established thermodynamic energy model into deep neural networks for identifying ncRNA families or motifs. The pre-training technique is broadly applicable to a range of network topologies as well as different types of ncRNA families and motifs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.