A Comprehensive Review on RNA Subcellular Localization Prediction
Cece Zhang, Xuehuan Zhu, Nick Peterson, Jieqiong Wang, Shibiao Wan·April 24, 2025
Summary
AI and ML offer scalable solutions for RNA subcellular localization prediction, addressing data scarcity. Innovations in high-throughput techniques and ML feature diversification show promise. Various approaches are highlighted, emphasizing their strengths, weaknesses, and integration with experiments for insights into RNA dynamics and potential therapies. Machine learning and deep learning techniques predict sub-cellular localization, with tools like lncLocPred, lncLocator, and PairProSVM contributing to the field. Studies employ different models to improve accuracy and efficiency in predicting subcellular localization of non-coding RNAs. Models for predicting ncRNA and miRNA subcellular localization are discussed, with a focus on deep learning for mRNA localization and tools for spatial transcriptomics. Key studies involve using machine learning and multimodal deep learning for disease diagnosis and prognosis.
Introduction
Background
Overview of RNA subcellular localization
Importance of scalable solutions in addressing data scarcity
Role of high-throughput techniques in advancing research
Objective
To explore AI and ML applications in predicting RNA subcellular localization
Highlighting innovations and their potential in non-coding RNA dynamics and therapy
Method
Data Collection
High-throughput sequencing technologies
Databases and repositories for RNA localization data
Data Preprocessing
Data cleaning and normalization
Feature extraction and selection techniques
Approaches and Tools
Machine Learning Techniques
lncLocPred: Predicting long non-coding RNA subcellular localization
lncLocator: Enhancing prediction accuracy for non-coding RNAs
PairProSVM: SVM-based method for ncRNA localization
Deep Learning Approaches
Deep learning models for mRNA subcellular localization
Tools for spatial transcriptomics analysis
Model Integration with Experiments
Combining AI predictions with experimental validation
Insights into RNA dynamics and potential therapeutic targets
Applications
Disease Diagnosis and Prognosis
Using machine learning for disease-specific RNA localization
Predictive models for personalized medicine
Non-coding RNA Subcellular Localization
miRNA subcellular localization prediction
Improving accuracy through multimodal deep learning
Case Studies
Predictive Models for ncRNA and miRNA
Comparative analysis of different prediction models
Evaluation metrics for model performance
Deep Learning for mRNA Localization
Advancements in deep learning techniques
Integration with spatial transcriptomics data
Conclusion
Summary of AI and ML advancements
Future directions in RNA subcellular localization prediction
Potential impact on biological research and medical applications
Basic info
papers
computer vision and pattern recognition
genomics
artificial intelligence
subcellular processes
Advanced features
Insights
How do tools like lncLocPred and lncLocator contribute to the prediction of RNA subcellular localization?
What are the main components of the machine learning models used for RNA subcellular localization prediction?
What role do deep learning models play in improving the accuracy of mRNA localization predictions?
What are the challenges in integrating high-throughput techniques with machine learning for RNA localization?