ReviewNon-coding RNA2020
Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives.
Review in Non-coding RNA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.Briefings in bioinformatics · 2026Review
- Plant long noncoding RNAs: why do we not know more?Biological research · 2025Review
- LGLoc as a new language model-driven graph neural network for mRNA localization.Scientific reports · 2025Article
- A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only.Bioinformatics and biology insights · 2025Article
- Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.EBioMedicine · 2024Review
- Unified mRNA Subcellular Localization Predictor based on machine learning techniques.BMC genomics · 2024Article
- MSLP: mRNA subcellular localization predictor based on machine learning techniques.BMC bioinformatics · 2023Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Bacteriophage classification for assembled contigs using graph convolutional network.Bioinformatics (Oxford, England) · 2021Article
- Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer.Non-coding RNA · 2021Review
- Vascular Endothelial Senescence: Pathobiological Insights, Emerging Long Noncoding RNA Targets, Challenges and Therapeutic Opportunities.Frontiers in physiology · 2021Review
- Noncoding RNAs in chronic wound healing: Mechanisms, exosome therapeutics, and translational frontiers.Journal of tissue engineeringReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regarding lncRNAome. To discover and understand lncRNAome, many sophisticated computational techniques have been leveraged. Recently, deep learning (DL)-based modeling techniques have been successfully used in genomics due to their capacity to handle large amounts of data and produce relatively better results than traditional machine learning (ML) models. DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well. In this review article, we summarized the contribution of DL-based methods in nine different lncRNAome research areas. We also outlined DL-based techniques leveraged in lncRNAome, highlighting the challenges computational scientists face while developing DL-based models for lncRNAome. To the best of our knowledge, this is the first review article that summarizes the role of DL-based techniques in multiple areas of lncRNAome.
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What OpenQuestion holds
Registered trials
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.