ArticleBriefings in bioinformatics2022
TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 56 citations in OpenAlex.
- RNA-Lexis: a probabilistic algorithm using a non-parametric segmentation logic to detect meaningful sequences in RNA.Nucleic acids research · 2026Article
- Regulation of immune checkpoint molecules in cancer immune evasion and therapy.Nature reviews. Cancer · 2026Review
- AutoGERN: single-cell RNA-seq gene regulatory network inference via explicit link modeling and adaptive architectures.Bioinformatics (Oxford, England) · 2026Article
- LncRNA Subcellular Localization Across Diverse Cell Lines: An Exploration Using Deep Learning with InexactNon-coding RNA · 2025Article
- Article
- lncRNA localization and feature interpretability analysis.Molecular therapy. Nucleic acids · 2025Article
- An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy.BMC biology · 2025Article
- CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines.Frontiers in bioinformatics · 2025Article
- LncSL: A Novel Stacked Ensemble Computing Tool for Subcellular Localization of lncRNA by Amino Acid-Enhanced Features and Two-Stage Automated Selection Strategy.International journal of molecular sciences · 2024Article
- Evaluation of machine learning models that predict lncRNA subcellular localization.NAR genomics and bioinformatics · 2024Article
- Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer.Biochemical genetics · 2024Article
- Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.BMC biology · 2024Article
- Article
- LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.Bioinformatics (Oxford, England) · 2023Article
- Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.Briefings in bioinformatics · 2023Article
- LncRNA CASC19: a novel oncogene involved in human cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Review
- RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies.Briefings in bioinformatics · 2023Review
- A First Computational Frame for Recognizing Heparin-Binding Protein.Diagnostics (Basel, Switzerland) · 2023Article
- A review from biological mapping to computation-based subcellular localization.Molecular therapy. Nucleic acids · 2023Review
- iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers.BMC bioinformatics · 2023Article
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which is responsible for their molecular functions, including cell cycle regulation and genome rearrangements. Accurately identifying the subcellular location of lncRNAs from sequence information is crucial for a better understanding of their biological functions and mechanisms. In contrast to traditional experimental methods, bioinformatics or computational methods can be applied for the annotation of lncRNA subcellular locations in humans more effectively. In the past, several machine learning-based methods have been developed to identify lncRNA subcellular localization, but relevant work for identifying cell-specific localization of human lncRNA remains limited. In this study, we present the first application of the tree-based stacking approach, TACOS, which allows users to identify the subcellular localization of human lncRNA in 10 different cell types. Specifically, we conducted comprehensive evaluations of six tree-based classifiers with 10 different feature descriptors, using a newly constructed balanced training dataset for each cell type. Subsequently, the strengths of the AdaBoost baseline models were integrated via a stacking approach, with an appropriate tree-based classifier for the final prediction. TACOS displayed consistent performance in both the cross-validation and independent assessments compared with the other two approaches employed in this study. The user-friendly online TACOS web server can be accessed at https://balalab-skku.org/TACOS.
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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.