ArticleBMC genomics2021
Identify RNA-associated subcellular localizations based on multi-label learning using Chou's 5-steps rule.
Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Structure-based virulence factor classification using a dual-driven graph transformer with a pretrained language model.Briefings in bioinformatics · 2026Article
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- LncLSTA: a versatile predictor unveiling subcellular localization of lncRNAs through long-short term attention.Bioinformatics advances · 2025Article
- GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs.Computational and structural biotechnology journal · 2024Article
- DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships.Biomolecules · 2024Article
- Inference of gene regulatory networks based on directed graph convolutional networks.Briefings in bioinformatics · 2024Article
- Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.Briefings in bioinformatics · 2024Article
- Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations.Briefings in bioinformatics · 2022Article
- Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction.International journal of molecular sciences · 2022Article
- EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction.Computational and structural biotechnology journal · 2022Article
- Identification of Vesicle Transport ProteinsFrontiers in genetics · 2022Article
- CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.PLoS computational biology · 2022Article
- A Multi-Objective Multi-Label Feature Selection Algorithm Based on Shapley Value.Entropy (Basel, Switzerland) · 2021Article
- Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.International journal of molecular sciences · 2021Review
- 4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism.Frontiers in cell and developmental biology · 2021Article
- Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm.Frontiers in genetics · 2021Review
- iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest.Frontiers in genetics · 2021Article
- KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest.Frontiers in genetics · 2021Article
- Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with LBioMed research international · 2021Article
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5 authors.
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Abstract
backgroundBiological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. However, lots of existing RNA subcellular localization classifiers only solve the problem of single-label classification. It is of great practical significance to expand RNA subcellular localization into multi-label classification problem.
resultsIn this study, we extract multi-label classification datasets about RNA-associated subcellular localizations on various types of RNAs, and then construct subcellular localization datasets on four RNA categories. In order to study Homo sapiens, we further establish human RNA subcellular localization datasets. Furthermore, we utilize different nucleotide property composition models to extract effective features to adequately represent the important information of nucleotide sequences. In the most critical part, we achieve a major challenge that is to fuse the multivariate information through multiple kernel learning based on Hilbert-Schmidt independence criterion. The optimal combined kernel can be put into an integration support vector machine model for identifying multi-label RNA subcellular localizations. Our method obtained excellent results of 0.703, 0.757, 0.787, and 0.800, respectively on four RNA data sets on average precision.
conclusionTo be specific, our novel method performs outstanding rather than other prediction tools on novel benchmark datasets. Moreover, we establish user-friendly web server with the implementation of our method.
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