ArticleBioinformatics (Oxford, England)2019
Prediction of mRNA subcellular localization using deep recurrent neural networks.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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51 citing papers in PubMed, 85 citations in OpenAlex.
- seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.Bioinformatics (Oxford, England) · 2026Article
- RNA-Binding Protein Occupancy Composition Predicts Long Noncoding RNA Subcellular Localization.International journal of molecular sciences · 2026Article
- seq2ribo: Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.bioRxiv : the preprint server for biology · 2026Article
- Predicting circRNA subcellular localization by fusing circRNA sequence and network information.Scientific reports · 2026Article
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- mRNALocator-imb: an imbalance-tolerant ensemble framework integrating random forest and transformer for mRNA subcellular localization prediction.Frontiers in genetics · 2026Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- LOCAS: multilabel mRNA localization with supervised contrastive learning.Briefings in bioinformatics · 2025Article
- LncRNA Subcellular Localization Across Diverse Cell Lines: An Exploration Using Deep Learning with InexactNon-coding RNA · 2025Article
- LGLoc as a new language model-driven graph neural network for mRNA localization.Scientific reports · 2025Article
- Decoding subcellular RNA localization one molecule at a time.Genome biology · 2025Review
- EDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance.BMC genomics · 2024Article
- Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization.Briefings in bioinformatics · 2024Article
- DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships.Biomolecules · 2024Article
- Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.Bioinformatics (Oxford, England) · 2024Article
- mRCat: A Novel CatBoost Predictor for the Binary Classification of mRNA Subcellular Localization by Fusing Large Language Model Representation and Sequence Features.Biomolecules · 2024Article
- Seq-RBPPred: Predicting RNA-Binding Proteins from Sequence.ACS omega · 2024Article
- Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments.iScience · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationMessenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. While new sequencing-based technologies allow the high-throughput identification of RNAs localized to specific subcellular compartments, the precise mechanisms at play, and their dependency on specific sequence elements, remain poorly understood.
resultsWe introduce RNATracker, a novel deep neural network built to predict, from their sequence alone, the distributions of mRNA transcripts over a predefined set of subcellular compartments. RNATracker integrates several state-of-the-art deep learning techniques (e.g. CNN, LSTM and attention layers) and can make use of both sequence and secondary structure information. We report on a variety of evaluations showing RNATracker's strong predictive power, which is significantly superior to a variety of baseline predictors. Despite its complexity, several aspects of the model can be isolated to yield valuable, testable mechanistic hypotheses, and to locate candidate zipcode sequences within transcripts. AVAILABILITY AND IMPLEMENTATION: Code and data can be accessed at https://www.github.com/HarveyYan/RNATracker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.