ArticleNucleic acids research2018
A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Position-dependent codon occurrence is associated with patterns of translational efficiency and ribosomal occupancy in the human transcriptome.PLoS computational biology · 2026Article
- Benchmarking reveals the superiority of nucleic acid foundation models in predicting lncRNA coding potential.Genome biology · 2026Article
- Uncertainty-aware benchmarking reveals ambiguous transcripts in mRNA-lncRNA classification.bioRxiv : the preprint server for biology · 2026Article
- Helixer: ab initio prediction of primary eukaryotic gene models combining deep learning and a hidden Markov model.Nature methods · 2026Article
- UTR-DynaPro: a CNN-transformer multimodal language model for decoding 5'UTR regulatory mechanisms.Scientific reports · 2026Article
- Nanomaterials in food safety and environmental monitoring: advances, challenges, and future directions.Mikrochimica acta · 2025Review
- LncPlankton: a comprehensive database of candidate lncRNAs from marine microbial eukaryotes.NAR genomics and bioinformatics · 2025Article
- Interpretation of RNA Universe and Coding Potential Using IntRNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency.Bioengineering (Basel, Switzerland) · 2025Review
- Longitudinal host-microbiome dynamics of metatranscription identify hallmarks of progression in periodontitis.Microbiome · 2025Article
- Machine learning approaches enable the discovery of therapeutics across domains.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations.BMC genomic data · 2025Article
- Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Current understanding of functional peptides encoded by lncRNA in cancer.Cancer cell international · 2024Review
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- ntEmbd: Deep learning embedding for nucleotide sequences.bioRxiv : the preprint server for biology · 2024Article
- Evaluating generalizability of artificial intelligence models for molecular datasets.bioRxiv : the preprint server for biology · 2024Article
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
- Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential despite being trained with less data and with no prior concept of what features define mRNAs. To understand what mRNN learned, we probed the network and uncovered several context-sensitive codons highly predictive of coding potential. Our results suggest that gated RNNs can learn complex and long-range patterns in full-length human transcripts, making them ideal for performing a wide range of difficult classification tasks and, most importantly, for harvesting new biological insights from the rising flood of sequencing data.
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