ArticleBMC genomics2024
Unified mRNA Subcellular Localization Predictor based on machine learning techniques.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 14 citations in OpenAlex.
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.Current drug targets · 2026Article
- Improving B-cell Linear Epitope PredictionCurrent drug targets · 2026Article
- A robust deep learning framework for RNA 5-methyluridine modification prediction using integrated features.BMC biology · 2025Article
- DeepBBMC biology · 2025Article
- PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi-source propensity scores.Protein science : a publication of the Protein Society · 2025Article
- A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only.Bioinformatics and biology insights · 2025Article
- EDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance.BMC genomics · 2024Article
- PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.Scientific reports · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost.
methodsIn this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER).
resultsThe proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection. AVAILABILITY: We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .
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