ArticleBriefings in bioinformatics2020
Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes.
Article in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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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Who cites it
4 citing papers in PubMed.
- A Novel Prognostic Ferroptosis-Related lncRNA Signature Associated with Immune Landscape in Invasive Breast Cancer.Disease markers · 2022Article
- Long Non-Coding RNAInternational journal of molecular sciences · 2021Article
- Emerging role of long non-coding RNAs in cancer precision medicine.Molecular & cellular oncology · 2020Article
- Prediction of RNA Methylation Status From Gene Expression Data Using Classification and Regression Methods.Evolutionary bioinformatics online · 2020Article
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Authors and funding
3 authors.
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Abstract
Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.
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