ArticleComputational and structural biotechnology journal2023
DNABERT-based explainable lncRNA identification in plant genome assemblies.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- LncPNdeep: A long non-coding RNA classifier based on large language model with peptide and nucleotide embedding.Non-coding RNA research · 2026Article
- Decoding plant physiology through systems biology: Integrative multi-omics and computational perspectives for next-generation crop design.Plant communications · 2026Review
- Large language models in bioinformatics: a comprehensive survey.Frontiers in genetics · 2026Review
- PolyA-GLM: A comprehensive framework forComputational and structural biotechnology journal · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- OMetaNet: an efficient hybrid deep learning model based on multimodal data fusion and contrastive learning for predicting 2'-O-methylation sites in human RNA.BMC bioinformatics · 2025Article
- Plant long noncoding RNAs: why do we not know more?Biological research · 2025Review
- Positional frequency chaos game representation for machine learning-based classification of crop lncRNAs.bioRxiv : the preprint server for biology · 2025Article
- Review
- Application of machine learning and genomics for orphan crop improvement.Nature communications · 2025Review
- Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning.BioData mining · 2024Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (
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Registered trials
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