ArticleInterdisciplinary sciences, computational life sciences2022
IDDLncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data Distributions.
Article in Interdisciplinary sciences, computational life sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- RNA-Lexis: a probabilistic algorithm using a non-parametric segmentation logic to detect meaningful sequences in RNA.Nucleic acids research · 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
- LncRNAs in neuropsychiatric disorders and computational insights for their prediction.Molecular biology reports · 2022Review
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Authors and funding
10 authors.
Funding
Abstract
Long non-coding RNAs play a crucial role in many life processes of cell, such as genetic markers, RNA splicing, signaling, and protein regulation. Considering that identifying lncRNA's localization in the cell through experimental methods is complicated, hard to reproduce, and expensive, we propose a novel method named IDDLncLoc in this paper, which adopts an ensemble model to solve the problem of the subcellular localization. In the proposal model, dinucleotide-based auto-cross covariance features, k-mer nucleotide composition features, and composition, transition, and distribution features are introduced to encode a raw RNA sequence to vector. To screen out reliable features, feature selection through binomial distribution, and recursive feature elimination is employed. Furthermore, strategies of oversampling in mini-batch, random sampling, and stacking ensemble strategies are customized to overcome the problem of data imbalance on the benchmark dataset. Finally, compared with the latest methods, IDDLncLoc achieves an accuracy of 94.96% on the benchmark dataset, which is 2.59% higher than the best method, and the results further demonstrate IDDLncLoc is excellent on the subcellular localization of lncRNA. Besides, a user-friendly web server is available at http://lncloc.club .
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Registered trials
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